{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f3efda35-7109-4380-9a3a-355c63ead341",
   "metadata": {},
   "source": [
    "# SVM(支持向量机)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "da4d448a-0b8c-4199-9747-63cdc9152307",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "\n",
    "# use seaborn plotting defaults\n",
    "import seaborn as sns; sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ec6feee2-6312-46da-83f0-c8fda94df808",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_blobs\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 生成样本数据\n",
    "X, y = make_blobs(n_samples=50, centers=2, random_state=0, cluster_std=0.60)\n",
    "\n",
    "# 绘制散点图\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c684560a-2245-41d2-b2a9-b541d8eb2ada",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xfit = np.linspace(-1, 3.5)\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')\n",
    "plt.plot([0.6], [2.1], 'x', color='red', markeredgewidth=2, markersize=10)\n",
    "\n",
    "for m, b in [(1, 0.65), (0.5, 1.6), (-0.2, 2.9)]:\n",
    "    plt.plot(xfit, m * xfit + b, '-k')\n",
    "\n",
    "plt.xlim(-1, 3.5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "99e33dab-98f2-4def-9b15-b3a8cc0518a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xfit = np.linspace(-1, 3.5)\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')\n",
    "\n",
    "for m, b, d in [(1, 0.65, 0.33), (0.5, 1.6, 0.55), (-0.2, 2.9, 0.2)]:\n",
    "    yfit = m * xfit + b\n",
    "    plt.plot(xfit, yfit, '-k')\n",
    "    plt.fill_between(xfit, yfit - d, yfit + d, edgecolor='none',\n",
    "                     color='#AAAAAA', alpha=0.4)\n",
    "\n",
    "plt.xlim(-1, 3.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a616e51-bb4f-46c9-8e7e-1cda9ddf9231",
   "metadata": {},
   "source": [
    "# 使用SVM对鸢尾花数据进行分析："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "52023404-77cf-4d5c-8d8c-9fc12f26e025",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集中的类别分布: (array([0, 1, 2]), array([35, 35, 35], dtype=int64))\n",
      "测试集中的类别分布: (array([0, 1, 2]), array([15, 15, 15], dtype=int64))\n",
      "分类报告：\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        15\n",
      "           1       1.00      0.93      0.97        15\n",
      "           2       0.94      1.00      0.97        15\n",
      "\n",
      "    accuracy                           0.98        45\n",
      "   macro avg       0.98      0.98      0.98        45\n",
      "weighted avg       0.98      0.98      0.98        45\n",
      "\n",
      "准确率： 0.9777777777777777\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import svm\n",
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# 加载数据\n",
    "data = pd.read_csv('D:/work/machine-learning/notebooks/PCA/iris.csv')\n",
    "\n",
    "# 处理标签列\n",
    "data['Species'] = data['Species'].str.split(': ').str[0]\n",
    "\n",
    "# 准备数据\n",
    "X = data.iloc[:, 0:4].values  # 特征列\n",
    "y = data['Species'].values   # 标签列\n",
    "\n",
    "# 将类别标签转换为整数编码\n",
    "le = LabelEncoder()\n",
    "y = le.fit_transform(y)\n",
    "\n",
    "# 分割数据集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=100, stratify=y)\n",
    "\n",
    "# 检查训练集和测试集中的类别分布\n",
    "print(\"训练集中的类别分布:\", np.unique(y_train, return_counts=True))\n",
    "print(\"测试集中的类别分布:\", np.unique(y_test, return_counts=True))\n",
    "\n",
    "# 训练SVM模型\n",
    "model = svm.SVC(kernel='linear')  # 使用线性核\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# 评估模型\n",
    "y_pred = model.predict(X_test)\n",
    "print(\"分类报告：\\n\", classification_report(y_test, y_pred))\n",
    "print(\"准确率：\", accuracy_score(y_test, y_pred))\n",
    "\n",
    "# 可视化结果（可选）\n",
    "# 由于数据是四维的，我们无法直接在二维或三维空间中可视化，但我们可以选择两个特征来展示\n",
    "# 这里我们选择Sepal.Length和Petal.Length进行可视化\n",
    "plt.scatter(X_test[y_test == 0, 0], X_test[y_test == 0, 2], label='setosa', alpha=0.5)\n",
    "plt.scatter(X_test[y_test == 1, 0], X_test[y_test == 1, 2], label='versicolor', alpha=0.5)\n",
    "plt.scatter(X_test[y_test == 2, 0], X_test[y_test == 2, 2], label='virginica', alpha=0.5)\n",
    "plt.xlabel('Sepal Length')\n",
    "plt.ylabel('Petal Length')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "83a623ba-5493-4253-b212-495a42d6e275",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('D:/work/machine-learning/notebooks/PCA/iris.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "0d909508-8f0b-4515-88f3-ba42baebd82d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "f3bf07c9-919c-4dfa-a743-b6f87b9e2bd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.preprocessing import KBinsDiscretizer\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "cae9c106-4c45-4a94-a2a9-67b2434c54ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "# 导入sklearn自带的iris数据集\n",
    "iris = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "190313df-432d-423c-9467-1dd4bbef4579",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=10000000000.0, kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(C=10000000000.0, kernel=&#x27;linear&#x27;)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "SVC(C=10000000000.0, kernel='linear')"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "# 导入sklearn自带的iris数据集\n",
    "iris = datasets.load_iris()\n",
    "from sklearn.svm import SVC # \"Support vector classifier\"\n",
    "model = SVC(kernel='linear', C=1E10)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "a689bb6d-bcf0-4ca7-8a55-646b4aec0ad5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-5 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-5 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-5 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-5 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=10000000000.0, kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(C=10000000000.0, kernel=&#x27;linear&#x27;)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "SVC(C=10000000000.0, kernel='linear')"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "# 导入sklearn自带的iris数据集\n",
    "iris = datasets.load_iris()\n",
    "\n",
    "# 特征数据和标签\n",
    "X = iris.data\n",
    "y = iris.target\n",
    "\n",
    "# 将数据划分为训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "# 创建SVC模型实例，使用线性核，C=1E10\n",
    "model = SVC(kernel='linear', C=1E10)\n",
    "\n",
    "# 训练模型\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# 现在模型已经训练好了，可以使用model.predict(X_test)来进行预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a0b80143-17e4-4d8b-8cf9-63be443f5677",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dfb37a92-5b9a-4bd5-82f4-be3f5819846a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy\n",
    "from scipy.cluster import hierarchy\n",
    "dendro=hierarchy.dendrogram(hierarchy.linkage(X,method='ward'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "b4457104-3e05-4209-baa1-f1b8357f7020",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID   Genre  Age  Annual Income (k$)  Spending Score (1-100)\n",
       "0           1    Male   19                  15                      39\n",
       "1           2    Male   21                  15                      81\n",
       "2           3  Female   20                  16                       6\n",
       "3           4  Female   23                  16                      77\n",
       "4           5  Female   31                  17                      40"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset=pd.read_csv('D:/work/machine-learning/notebooks/clustering-model-example/Mall_Customers.csv')\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c521c2cc-16fb-442a-98bd-45b25b3a4eb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zyc92\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\zyc92\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\zyc92\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\zyc92\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\zyc92\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\zyc92\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\zyc92\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\zyc92\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\zyc92\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'wcss: sum of dist. of sample to their closest cluster center')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "wcss=[]\n",
    "for i in range(1,10):\n",
    "    kmeans=KMeans(n_clusters=i,init='k-means++',)\n",
    "    kmeans.fit(X)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "    \n",
    "plt.plot(range(1,10),wcss)\n",
    "plt.title('Elbow Method')\n",
    "plt.xlabel('No. of cluster')\n",
    "plt.ylabel('wcss: sum of dist. of sample to their closest cluster center' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "887efe6a-e3cf-46a6-8677-b619e552a7b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zyc92\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "kmeans_1=KMeans(n_clusters=5)\n",
    "kmeans_1.fit(X)\n",
    "cluster_pred=kmeans_1.predict(X)\n",
    "cluster_pred_2=kmeans_1.labels_\n",
    "cluster_center=kmeans_1.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "52b9d92a-c707-4594-8c31-dcf4891c1c23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 4, 4, 4, 0, 4, 0, 4, 0, 4, 0, 0, 0, 0, 4, 0, 4,\n",
       "       0, 0, 4, 0, 4, 0, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 4, 0, 4, 4, 4,\n",
       "       0, 0, 0, 4, 0, 0, 0, 0, 0, 4, 0, 0, 2, 4, 3, 2, 2, 3, 0, 3, 2, 3,\n",
       "       2, 2, 2, 4, 2, 2, 2, 3, 3, 4, 2, 4, 3, 4, 2, 3, 4, 4, 2, 3, 3, 3,\n",
       "       2, 4, 4, 3, 2, 2, 4, 2, 2, 2, 4, 2, 2, 2, 4, 2, 2, 4])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 聚类结果\n",
    "cluster_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "2552e1b6-39f8-415d-9397-16bcc152bf72",
   "metadata": {},
   "outputs": [],
   "source": [
    "#把聚类结果关联回原始数据集（未标准化的数据）\n",
    "cluster = pd.DataFrame(cluster_pred,columns=['cluster'])\n",
    "df_cluster = pd.concat([dataset,cluster],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "07585441-534b-4766-a643-22bf9a8eec27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID   Genre  Age  Annual Income (k$)  Spending Score (1-100)  \\\n",
       "0           1    Male   19                  15                      39   \n",
       "1           2    Male   21                  15                      81   \n",
       "2           3  Female   20                  16                       6   \n",
       "3           4  Female   23                  16                      77   \n",
       "4           5  Female   31                  17                      40   \n",
       "\n",
       "   cluster  \n",
       "0      1.0  \n",
       "1      1.0  \n",
       "2      1.0  \n",
       "3      1.0  \n",
       "4      1.0  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cluster.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31fa4347-9af9-4a52-8968-2e15ce03e1a4",
   "metadata": {},
   "source": [
    "# 对鸢尾花进行聚类分析："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "af01f1a7-0941-40b2-b81c-30bad7999fef",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zyc92\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1446: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Silhouette Score: 0.5528190123564095\n",
      "Adjusted Rand Index: 0.7302382722834697\n"
     ]
    },
    {
     "data": {
      "image/png": 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OkyZNIiYmhvbt2+d7v2eeeYYdO3bwwAMPOFZQtlqtrFq1ilmzZtG5c2fHHI2rtW/fnrCwMN59912eeuop0tPT+fTTT/Ncgt++fXvGjRvHiBEjuO+++zCZTHz//feYzWbHVWDR0dGcO3eOlStX0qBBA7p06eK4RP3xxx+nVq1abN++nU8++YQbbriBuLg4w8+LEffffz/ffvstDz/8sGPy7GeffUZ2drbLid25Ro4cyR9//MH999/P8OHDMZvNfPPNNxw7doxJkyYVaIxGtWnThq+++opy5crRokULzpw5w5QpU2jbtq3H52337t2OI1WZmZns2bOHqVOnEhISkucKwqZNm7JgwQIaNWpEpUqV2Lp1KxMmTEAI4dhnc+cxrVu3jlq1atGsWTND+7qn+I3uG9HR0WzdupV169bRsGFDl3/HSmBSyY6iYD8MPmbMGF599VVefvllPvnkE6c2tWvXpm7duiQmJjrm4Ljy1FNPUb58eWbMmMGkSZMoU6YMHTp04JlnnnF8GHz++ef8+9//ZsyYMZjNZmrXrs348eN555132LRpk+HyE507d2bcuHF89dVXjBw5EiEErVq1Ytq0aS7X/8kVHR3N9OnT+eabb/j111/5/vvvkVJSrVo1XnjhBe644w7Ht/CrRUVF8fHHH/P+++8zYsQI4uPjGTlyZJ7Lw+vXr88XX3zBZ599xjPPPIPNZqNx48Z89dVX1KxZE4CBAweycuVKRowYwRNPPMHw4cP58ssv+eijj5gwYQLnz5+nYsWKDB06NM+VPAUlOjqaadOm8e9//5vRo0cTERHB4MGDCQ8PdzsZuE6dOsyYMYMPPviAF198ESEETZs2Zdq0aS5X2faHJ598ErPZzNy5c/nss8+Iioqie/fuhq76GjlypOPfkZGRVK5cmVtvvZUhQ4bkmSv07rvv8tZbbzmO0FWvXp033niD+fPnO65sjIyM5MEHH2TmzJmsWLGCNWvWGNrXPcWvaZqhfePee+/l77//5pFHHmHs2LH069evQJ5fpeQTUlVNUxQlAG3bto3k5OQ885asVitdu3alb9++vPDCC0UYnaIoBUkd2VEUJSCdPHmSp59+mhEjRtC2bVsyMzP5/vvvSU1N5c477yzq8BRFKUDqyI6iKAHru+++Y8aMGRw7dozg4GCaNWvGk08+SZMmTYo6NEVRCpBKdhRFURRFKdXUdXqKoiiKopRqKtlRFEVRFKVUK1bJzqFDh2jRogU//PBDvm1+/PFH6tWr5/Rz5MgRP0aqKIqiKEpJUWyuxsrJyWHUqFGOIn352bNnD23btuWDDz7Is72gFxxTFEVRFKV0KDbJzieffEJERITHdnv37qV+/foei/V5Q0qJrpfcedqaJkp0/L4K1HGDGnsgjj1Qxw2BO/ZAHTcYG7umCbernV+pWCQ7GzduZObMmcybN89lccUr7dmzh169ehVo/7ouSUkxvkR/cWIyaURHh5GebsFmM15TqaQL1HGDGnsgjj1Qxw2BO/ZAHTcYH3t0dBgmUwlJdlJSUhg9ejQvv/yyo+Bcfi5cuMC5c+fYuHEj06dPJzk5mWbNmjFq1Chq1KjhcwyaJoiN9XxUqTiLjg4r6hCKRKCOG9TYA1GgjhsCd+yBOm4o2LEXebLz+uuv07x5c0M1TPbu3QvYCzf+5z//ISMjg88//5zBgwezYMGCPHVcvGE/suN+rlBxlZsBp6RkBlT2H6jjBjX2QBx7oI4bAnfsgTpuMD52+5EdY9dZFWmyM2/ePDZt2sSCBQsMtW/fvj0bNmzIU8n2s88+o1u3bvzwww8MHz7c51is1pK9M9lseokfgy8Cddygxh6IYw/UcUPgjj1Qxw0FO/YivfR87ty5nD9/nq5du9KiRQtatGgBwGuvvUbfvn1d3ufKRAcgPDychIQEzpw5U+jxKoqiKIpS8hTpkZ1x48ZhsVjybOvZsydPPPEEffr0cWo/Y8YMPvroI1auXEloaCgAaWlpHD58mEGDBvklZkVRFEVRSpYiPbJTsWJFqlWrlucHoGzZssTHx2Oz2UhMTHQkRN26dUNKyejRo9m3bx87duzg//7v/4iLi2PAgAFFORRFURRFUYqpYrWC8tVOnTrFDTfcwK+//gpA5cqV+frrr0lPT+eee+5h6NChREVFMW3aNMeRHkVRFEVRlCsV+dVYV9uzZ4/j3wkJCXl+B2jQoAGTJ0/2d1iKoiiKopRQxS7ZURSlaGWkZnJq/2mEgMq1KxEWGbjrfCiKUjqoZEdRFADOn7jA3Pd/YvXsdVizrQCYQ810vvt6BjzTj9iKMUUboKIoio+K9ZwdRVH849SB07x44+v88f0aR6IDkG3JZtn0lbzc800Sj50rwggVRVF8p5IdRQlwUko+GPop6Rcz0F2sVqrbdC4mpvDx8C+KIDpFUZRrp5IdRQlwu9fu4cTeky4TnVy6TefAloMc/OuQHyNTFEUpGCrZUZQAt2nhFkxBnt8KNJPGpoVb/RCRoihKwVLJjqIEOEuaxXMjQGiCTINtFUVRihOV7ChKgIupFIOUnttJm05spZhCj0dRFKWgqWRHUQLcDYM6uJ2vk0sC19/evvADUhRFKWAq2VGUAFeldmXa9GmJpuX/diA0Qac7OlK2SpwfI1MURSkYKtlRFIXHPh1G3Xa1AdA04diumexvEU06N+Kh9+4vktgURVGulVpBWVEUQiNCeXH2KDb8vJnfJy/l8I4jIAS1mteg58M97Ed+TOq7kaIoJZNKdhRFASAoOIiOA9rRcUC7og5FURSlQKmvaoqiKIqilGoq2VEURVEUpVRTyY6iKIqiKKWaSnYURVEURSnVVLKjKIqiKEqpppIdRVEURVFKNZXsKIqiKIpSqqlkR1EURVGUUk0lO4qiKIqilGoq2VEURVEUpVRTyY6iKIqiKKWaSnYURVEURSnVVLKjKIqiKEqpppIdRVEURVFKNZXsKIqiKIpSqgUVdQCKohiXlpzOuaOJlIkJJ7pSLCZzcFGHpCiKUuypZEdRSoAzh8/yw/vzWfvjemw5NgBCI0Lock8nBjzTj+iyUUUcoaIoSvGlTmMpSjF3dNdxXrrpDdbM/dOR6ABY0rNYPGUZr/R6i6QzyUUXoKIoSjGnkh1FKcZ0m864IR9hSc9Ct+kubz9/8gLjR04qgugURVFKBpXsKEoxtnXJds4dP+8y0cml23T+/mMXJ/ad8mNkiqIoJYdKdhSlGNu8cAtakOc/U00TbF601Q8RKYqilDwq2VGUYiwz3YLUpcd2wqRhSbP4ISJFUZSSRyU7ilKMxVaMQWjCYzvdqhNbKabwA1IURSmBVLKjKMVYpzuvR7fmP18nlxak0a5/Gz9EpCiKUvKoZEdRirEaTavRuEtDNFP+f6pCCHrc31WttaMoipIPlewoSjH35MTHqdG0GkCeU1q5CVDr3i247427iiQ2RVGUkkCtoKwoxVxEmXBe/WkMf87fyG+Tl3J89wk0k6BO69r0fKg7LXo2Q9PU9xZFUZT8qGRHUUqA4JBgOt3RkU53dCQoSCM2NoKkpHSsBubzKIqiBDr1dVBRFEVRlFJNHdlRFBd0XefEnpNY0rOIrRRDuYSyRR2SoiiK4iOV7CjKFXSbzm+Tl7Jwwu+cO37esb1Bh3rc9vQtNOnSqAijUxRFUXyhTmMpyiW6Tefj4eOZ/up3eRIdgH/W72XsXe+zYsaqIopOURRF8ZVKdhTlkkUTl7Dhl83gojqD1CVImPjsVE7sPen/4BRFURSfqWRHUbAf1fl1wu8uE50rCSFYPGW5f4JSFEVRCoRKdhQFOLr7OBdOXvDYTrfprF+w0Q8RKYqiKAVFJTuKAmSmZhpua0lX1cUVRVFKEpXsKAoQVznWcNuYCjGFF4iiKIpS4FSyoyhAxeoVqNWyZp7aU64ITdDt3k5+ikpRFEUpCCrZUZRLBjx9i/2qq3xoJkF4dDhdB6tkR1EUpSRRyY6iXNKyZ3MeeOdeEJcriucSmiAsKpwXZj1LdLnoIopQURRF8YVaQVlRrtDr4R7Ub1+H379axoafN5OVkUVMxTJ0v7cz3e7rQpnyKtFRFEUpaVSyoyhXqdaoKo+8P5RH3h9a1KEoiqIoBUCdxlIURVEUpVRTR3YURbkmUkpOHzpL2oU0IuMiqVSjAkK4v6pNURTFn1SyoyiKz9b+8Cc/ffwrx3Yfd2y7rkEC/f+vD9ff3r4II1MURbmsWJ3GOnToEC1atOCHH37It01SUhLPPvssbdq0oU2bNrzyyitkZGT4MUpFUQC+//dcPn3sS47vOZFn+/E9J/js8S/5/u05RRSZoihKXsUm2cnJyWHUqFEeE5cnnniCY8eOMXXqVD7++GPWrFnDG2+84acoFUUB2Lp4G/M//gXAaW2i3N/nf/IrW37/y9+hKYqiOCk2yc4nn3xCRESE2zZbt25lw4YNjB07lkaNGtGhQwfefPNNfvrpJ86cOeOnSBVF+XXC705rEV1NM2ks/HKxnyJSFEXJX7GYs7Nx40ZmzpzJvHnz6Nq1a77tNm3aRPny5alVq5ZjW9u2bRFCsHnzZvr06eNzDEFBxSbv84rp0geOycMHT2kTqOOGoh+7JT2Lnat2e2yn23R2rtqN1ZJNaGRogfRd1GMvKoE6bgjcsQfquKFwxl7kyU5KSgqjR4/m5ZdfpnLlym7bnjlzxqmN2WwmJiaGU6dO+RyDpgliY90fVSruoqPDijqEIhGo44aiG3tSdo5X7UOCtQL/+wrU1z1Qxw2BO/ZAHTcU7NiLPNl5/fXXad68Of369fPYNjMzE7PZ7LQ9JCSErKwsn2PQdUlKSsmc5GwyaURHh5GSkonNphd1OH4TqOOGoh+7FUGQOQhrttVj2yBzEFYESUnpBdJ3UY+9qATquCFwxx6o4wbjY4+ODjN89KdIk5158+axadMmFixYYKh9aGgo2dnZTtuzsrIIDw+/plis1pK9M9lseokfgy8CddxQhGPXNK4f2I5Vs9ehu3kj0kwaHQe0Q5hMBR5noL7ugTpuCNyxB+q4oWDHXqQnA+fOncv58+fp2rUrLVq0oEWLFgC89tpr9O3b16l9pUqVOHv2bJ5t2dnZJCcnU7FiRb/ErCgK9Hm0F0ITkN/agcJePLXPo738GpeiKIorRXpkZ9y4cVgsljzbevbsyRNPPOFysnGbNm0YN24cR44coVq1agCsX78egJYtWxZ+wIqiAPaFA5+aPIKPhn2ObtPzHOHRTBqapvHk5Mep2jChCKNUFEWxK9JkJ7+jMWXLliU+Ph6bzcaFCxeIiooiNDSUZs2a0bJlS55++mlef/11MjIyeO2117jtttvUkR1F8bNWvZozbvW/WfL1clbNWkt6cgYRMeHccEdHbhrajQrVyhd1iIqiKEAxmKDszqlTp+jRowdjx45l4MCBCCH49NNPeeONN3jggQcICQnh5ptv5oUXXijqUBUlIFWoVp7Br97J4FfvLOpQFEVR8lXskp09e/Y4/p2QkJDnd7Af9fn444/9HZaiKIqiKCVUsUt2FCVQ6LrO1sXbuJiYSpXalajfvm5Rh6QoilIqqWRHUfzMarXyxcjJrF+wEdsVl1WGRoTQ59FeDBp9W9EFpyiKUgqpZEdR/MhqtTKq40ucPZLodJslPYsf3p/PsX+O8/RXI4sgOkVRlNIp8IpuKEoRmvjUVJeJzpU2/rKFVbPW+CkiRVGU0k8lO4riJ7qus27eekNt546bX8jRKIqiBA6V7CiKn+xeuwdrjs1QW09HfxRFURTjVLKjKH5y8exFr9rremDWw1EURSloKtlRFD+Jrx9vuK0QAk1Tf56KoigFQb2bKoqfVGt4HRExEYba1m5ds5CjURRFCRwq2VEUP7rtqVsMtRvy1j2FHImiKErgUMmOovhR38d60eWeG9y2efA/Q6jdQh3ZURRFKSgq2VEUP/vX/x7imakjSagfD8K+TWiC+h3q8s7S17lpaLeiDVBRFKWUUSsoK0oRaN27Ja17tyzqMBRFUQKCOrKjKIqiKEqppo7sKAFhz/p9/LV0O6ERIXQd3Iky5csUdUjFVkZqJqf2n0YIqFy7EmGRYUUdkqIoBU1KNI6ikYJODLq4rlC60eQxNJLRiUKnGghRKP14opIdpVT7+fNFzH73B3KyrI5tM9/5gdjKMbwy73kqVa9YhNEVL+dPXGDu+z+xevY6rNn258scaqbz3dcz4Jl+xFaMKdoAFUW5dlJilgsJk1MJ4oBjs5V6ZIqhZGs9C6Qbs1xMmD6FIPZc0UdNLOJ+ssQtfk961GkspdSa+sK3zHhjVp5EJ1fSqWSeaf8Cx/eeLILIip9TB07z4o2v88f3axyJDkC2JZtl01fycs83STx2rggjVBSlIITLj4iSr2DiYJ7tJvYSJV8gTP/imvsI078kSh+Dib1X9XGISPk64foHIOU19+MNlewopdLpw2f4/aul7htJeLX32/4JqBiTUvLB0E9Jv5iBbnMuUaHbdC4mpvDx8Gt/E1QUpegEy+WEyekACPImG7m/h8uJBMt1PvcRJDcQLie47SOMGZhZ5nMfvlDJjlIqffbol4baWdIs7Nu0v5CjKd52r93Dib0nXSY6uXSbzoEtBzn41yE/RqYoSkEK02cgPXzsSzRC9RnX2IfJQB/f+tyHL1Syo5RKh3ccMdz2+3d+KMRIir9NC7dgCvL8VqCZNDYt3OqHiBRFKWhCphLMFgTuCwwLdIJZBzLb+05kDsGsRmAz0Mc2hPSuOPK1UMmOUirpuvHzwZmpmYUYSfFnSbMYaic0QabBtoqiFC+CDC/aSgTevy8KLE6nrgoqpmulkh2lVAoyG7/QsFxC2UKMpPiLqRRjaK6gtOnEVoop9HgURSl4OmWQBi/AlpiRGCtanPd+4UhCDLY1oRPjdR++UsmOUiq1urmF4bYPvzekECMp/m4Y1MHtfJ1cErj+9vaFH5CiKAVPhJLNjQbm05guXRruw8o0wn5fI31k0wOE/9bwUsmOUio9+r+HDLWrXKtiwC8wWKV2Zdr0aYmm5f92IDRBpzs6UrZKnB8jUxSlIGVq9i92Etdr3Ni3a1jEYN/7EIMBk4c+IFO73+c+fKGSHaVUMoeZGf3tU27bhJcJ571V6tJzgMc+HUbddrUB0LTLb1Kayf4W0aRzIx56z79vToqiFCybqE+a9i72ZCTv0Rf7VVrBpGrvYxM1fO5DF9VJ1d4Hgp2u/LL3aSJNG4tNNPC5D1+oZEcptZrf2JT/bfgPNZtX58ovGUHmILrd15kJuz/CZHJ/uDVQhEaE8uLsUYz84l/UblULc2gw5jAz9drW4clJjzN6xlOYQ4OLOkxFUa5RtuhOsjYHi7gHnTgkweiUwyKGkKzNJUdcf8195IiOJGs/YBH3Y6PcpT5isYi7SdbmkC16FMBIvCOk9PMyhsWQzaZz4UJ6UYfhk6AgjdjYCJKS0rFaPc+7KC0Cddygxh6IYw/UcUPgjj1Qxw3Gxx4XF4HJZOyYjTqyoyiKoihKqaYKgSoBIeVcCmePJBJkDiK+bhWCQwr+lEzqhTTOHD6LKchEfJ3KmMPMbttLKTmx9ySZqRZiKpShfNVyBR6ToiiKopIdpZQ7uvMYc9+fz6aFW5CXFhqMiAnnxge6ceuTfQmNCL3mPk7sO8UP435i/YJNjku4w6LC6D6kC7c9dQsRZcLztNd1nWXTV/LL54s4czjRsb1O61rc9tQttLip2TXHpCiKolym5uyg5uyUREbGvXvtHt69+wNsVpvTOjKapnFdwwRemfc84VG+r/Wwf8tB/n37f8nJynHuw6RRqUZFXlvwAlFxkYA90Znw5FesmrUWIQRX/vkJTSB1yf1v38PNj9yUb5+B+ppD4I49UMcNgTv2QB03qDk7imKYJd3C+w98jDXH6rqSt65zbPdxpr3se8G7nKwcxg35mByLc6ID9uKZpw+dYdKoqY5tK2esZtWstQBc/T0j98jTtFe+UwU3FUVRCpBKdpRSac3cP8lIyXQkEK7oNp01c9aRcj7Vpz42/rKZlHMp6Lr7auGbft3K+RMXkFLyy/jfyGetLQdN0/ht8lKfYlIURVGcqWRHKZU2/rIZITxkFYDNqrNt2Q7f+li4Jc8CfPmRSLb8/hdnjyRycv8pPNXJ0206G37Z7FNMiqIoijOV7CilUnpKptNpIpeE8arfV8tMtRiqrq5pGplpFq+qq2dnZBuLX1EURfFIJTtKqVQ2Ps5R6sAtCTEVY3zqI65SDFqQ5z70S9XCveknulyUoSNTiqIoimcq2VFKpc53djRUyTu8TDjNezTxqY9Od16PbuAqCXOYmda9WxBToQyNOzf0mIRpJo2ugzv5FJOiKIriTCU7SqnUvEdTrqsf7zGx6P9/fXxeYLB+h7rUblXTYx99/tWTsEj75e23PtnX7YRmoQnMYWZuHNrNp5gURVEUZyrZUUolzaTx/PfPUKFaefsGkfc2gBsf6Eq/kb197kMIwbNfP0F83Sr2311UC7/hjg4MGn2bY3ujGxow/MMHEZpwSpKEJggJD+H5756mbJU4n+NSFEVR8lIrKCulVlzlWN5Z8jqr56xl8ZRlnD5oL+XQqFMDej3cg0adGlzzvJgy5aN5a+HLrP1xPb99tZST+06hmTQatK9Lr2E30rRbY6c+ut7TidotarJ46jLWzduAJS2L6PJRdB3ciR73dyXWxzlEiqIoimtqBWXUCsolUaCOG9TYA3HsgTpuCNyxB+q4Qa2grCiKoiiK4jV1GiuAWLOtHN97EmtWDuWrlqdM+eiiDgmA5LMXOXfsHEEhwSTUq0JQsPvdUrfpHN17glMmjZCocKIrlPFTpIqilDZCJmHiBBITNmqCCCnqkJRCoJKdAGBJz2LBp7+yeMpy0pLSAPtk2Fa9mjPw2f5Ub1KtSOI6+Nchfnh/PlsXb3csoBdVNoqeD3Wn34jemMPMedpbc6wsnLCYRZMWk3Qq2bG9ceeGDHimHw061PNn+IqilGAmeYAwfSJmliKwnyrRiSRL3E6meAgpIos4QqUgqTk7lO45O5Z0C28NeI/DO4441YnSTBqaSeO5b56kSZdG/goZgG3LdjDu/o+RunRaD0dogtota/Li7FGEhNu/ZVlzrIwb8jE7Vux0WllYM2lIKRnx+XA6DmjntzEUFXUuP/DGHqjjhsIZe5DcRrT+OJCDwJbnNomGjeqkaJORouiOfqvXXM3ZUbzwzeszObLjqMuCmLpNx2a18eGDn5KRkuG3mFIvpPHhQ5+hW3WXC/9JXbJ/y0G+//dcx7b5H//qMtEB+zikLhk/chKJx84VauyKopRw0kKU/gyQ7ZToAAh0TBwmQh/r/9iUQuP1aSyr1cqGDRtYt24dx48fJzU1ldjYWKpUqULnzp1p2bKlWua+mEhLTueP71a7XcRO6hJLRharZq+j18M9/BLXHzNXk2PJcVv7SeqS5d/8wZ0vDCDYHMyiiUs81oqSUrJ02krufun2gg5ZUZRSIkQuRiPZbRuBjpklCPkMUpT3T2BKoTJ8ZCc7O5uvv/6aG2+8kYceeohZs2Zx+PBhMjIy2L17NzNmzODee++lS5cuTJ8+nezs7MKMWzFgx4qdWHOcv7m4svGXTYUczWUbft5sqMhltiWbv//Yzf4tBx1zjdzRbTrrF2wsiBAVRSmlzHIFEiNfyHXMclWhx6P4h6EjO9u3b+f555/HZDJxzz330Lt3b6pWrerUbs+ePaxcuZLp06czbdo03nvvPVq0aFHgQSvGGK7mLSH9ovGK3Ncq/aLxU2aWdAtBwSbD7b2pLK4oSiBKQ2BkqqqGwH+n95XCZSjZee655xg1ahQ33XST23b16tWjXr16DB8+nF9//ZUxY8bw22+/FUigivdiKsUYaqeZBGXjYws3mCuUiy/LqQOnXc4julpsxRgi4wxeFSEgtrL/xqEoSskjRUWkNLmcr3MlgY6OOoVVWhg6jbVgwQKPic7V+vTpw4IFC3wKSikYTTo3JMpAoqDbJF3uvsEPEdl1vqujsUSnUgwNr69PtUbXEV+nMkaOPHdT1cIVRXEjS/T1mOgASMLIFp39EJHiD4aSHbPZ7LlRAd5PKRhB5iD6P9nXbRvNpFG5diVa9mzun6CAtre0pmL18h6rhd/21C1oJg0hBAOe7Y+7I8+aSaNMuWhuuKNjAUerKEppkkMbrNRHkv/pcQlkivtBhPkvMKVQ+bSo4G+//caWLVtISUlxuk0IwTvvvHPNgSkFo8+/enLu2Dl+m7QUzaRdvtT70lGScglleWHms5iCjM+LuVZB5iBemDWKtwb8hwsnLwDCMWE5N8Z+I3tz49Bujvt0HNCOc8fO8f2/5+YdB/Z1eSJjI3hx9ijCo9Sbk6IobgiNFO0jovXHMHEQEI45PBL76a0s+pMphhVtnEqB8npRwXHjxjFp0iQiIyOJjnZecEkIwdKlSwssQH8ozYsKgv2S7N1r9/Db5KXsWLkTW46NitXLc9OD3bnhjg6ERRZNgpCRmsmqmWtYPHU5iUfPEWQOomnXRvQadiP129d1eZ8DWw/x+5SlbF70FzmWbOKqxNF9SBe63tPJ0Cm70kAtNhZ4Yw/UcUMhjl1mEiIXEipnY+IIoJFDayzaXeTQHop4CRX1mhfsooJeJzvXX389PXr04M033/TmbsVaaU92SqNAHTeosQfi2AN13BC4Yw/UcUMxWUE5KyuLm2++2du7KYqiKIqiFAmv5+z07NmTZcuW0bGjmgiqFA1rtpXNC7ehZ1upULMS1QwUMj2x9yR7N+4nJMxM8xubEh4d7ra9ruuc2HMSS3oWsZViKJdQtqDCvyZpyemcO5pImZhwoivFYjIHF3VIiqIoxZ7Xyc6LL77IHXfcwZAhQ2jWrBmhoaF5bhdCMGLEiAILUFFyWTIsfDRsPNuX/53n0vWImAhuf+5Wbh52o9N91s/fyLRXviPpdPLljQLqtK7F01+NIKZCTJ72uk3nt8lLWTjhd84dP+/Y3qBDPW57+ha/F0zNdebwWX54fz5rf1yP7dKq2KERIXS5pxMDnulHdNmoIolLURSlJPB6zs748eP56KOP8n9AIdi9e7fhxzt//jzvvvsuq1atIisrizZt2jB69Ghq167tsv2PP/7ImDFjnLb//vvvVKvm+Ru+K2rOTvFnybDwRKvRpF3Iv2xEz4d7MPSdex2/L/pyMdNe+S7f9sGhwXywbixlq8QB9kTn4+Hj2fDLZqfL3IVmv2Js+AcP0tXPa/kc3XWcN28diyU9y6lwqmbSKFsljtd/eZHYijF+jasoBMr+frVAHTcE7tgDddxQTObsfPPNN/Tt25fVq1fzzz//OP14k+gAPPbYYxw7doyJEycyZ84cQkNDGTp0KJmZrpf937NnD23btmX16tV5fhISErwdilKCjLvvY7eJDsDvk5eyZ8M+AJLPJrtNdAByLDm8PfA9x++LJi5xmeiAvTApEiY+O5UTe096PwAf6TadcUM+cpno5N5+/uQFxo+c5LeYFEVRShqvk52MjAzuvPNOypUrd82dJyUlkZCQwFtvvUWTJk2oVasWjz/+OImJiezbt8/lffbu3Uv9+vUpX758nh+TyX/rxCj+ZUmzsGvtP4baznhjFgDfvj7LUPszh85ycv8pdJvOrxN+d7twIdiPXC6estzQYxeErUu2c+74eZeJTi7dpvP3H7s4se+U3+JSFEUpSbyes9OxY0fWr19Pu3btrrnz2NhYPvjgA8fv586dY/LkyVSqVCnf01h79uyhV69e19z31YKCvM77ioXcQ3hGD+WVRKtnr/WYhOQ6+NdhgoI0ti3bYfjxF325mJ4Pdru0wKF7uk1n/c8bGfbfIYYf/1ps+W0rWpCG7uEwtqYJtv7+F9UaxPslrqISCPu7K4E6bgjcsQfquKFwxu51snPrrbfy8ssvc+TIEVq0aEFkpPNCbrfddpvXgbzyyivMmjULs9nM+PHjCQ93vlrmwoULnDt3jo0bNzJ9+nSSk5Np1qwZo0aNokaNGl73mUvTBLGxET7fvziIji69KwfnZBis3o49GYmNjcCabTX++JnZbhaOd5aVnuW3/cWWbTVUR0wzaWC1lfj92KjSvL+7E6jjhsAde6COGwp27F4nO0888QQAv/zyC7/88ovT7UIIn5KdBx54gLvuuovvvvuOESNGMGPGDBo1ynvly969ewEwmUz85z//ISMjg88//5zBgwezYMECn0+t6bokJSXDp/sWNZNJIzo6jJSUTGxuTnWUZGWvM/66BocEkZSUTnh0OJb0LEP3iakcQ7AXq0iXqVCGpCT/TGiPjItE0wQ2DwmPzaoTFhPht7iKSiDs764E6rghcMceqOMG42OPjg4zfPTH62SnsEpB5J62euutt/jrr7/45ptvGDt2bJ427du3Z8OGDZQpU8ax7bPPPqNbt2788MMPDB8+3Of+S/psd5tNL/FjyE/rW1oTFGzCmuO5UnGLns2wWnW63HMDP36wwNDj9xvZh/DocGq1rMnBvw65PZIiNEG3wZ389lxfP6gjv05Y7LGdFqTR5pbWpXYfuFpp3t/dCdRxQ+COPVDHDQU7dq9PiMXHxxMREcG+ffuIj48nPj4eXddZunQpUVFRxMcbnzNw/vx5fv75Z2y2yx9imqZRq1Ytzp496/I+VyY6AOHh4SQkJHDmzBlvh6KUEJqm0f3+rh7bCSG47427ARjwdD+CzJ5z+WbdGzsWGBzw9C1uEx3NJAiPDvfrpec1mlajcZeGbivECyHocX9XtdaOoihKPrxOdvbv388tt9ySpzbWiRMn+O9//8vAgQM5fvy44cc6e/Yszz77LBs2bHBsy8nJYdeuXdSqVcup/YwZM2jXrh0Wy+U5HGlpaRw+fDjfCc1K6TD0nXtp2q1xvrcLIXhq8uOONXOCzEG8vuBFt9Xc4+tW4blvn3L83rJncx54514QOCUXQhOERYXzwqxniS7nXAC3MD058XFqNK3miCNXboyte7fgvjfu8mtMiqIoJYnXiwoOHz6cixcv8umnn1K+fHnH9gsXLvD4449TsWJFt4sOXklKybBhwzhx4gRvv/020dHRfPHFF6xevZp58+ZRsWJFLly4QFRUFKGhoZw6dYpbb72V9u3b83//939YLBY++OADjh8/zoIFC5xWczZKLSpYcqz8fg0/fjifs4cTATAFmWjStRFD3ryHyrUqOrW/mJjC9Fe+Y+Ovm8nJsk9aLlOhDH0e7Unfx3qhac75/pGdR/n9q2Vs+HkzWRlZxFQsQ/d7O9Ptvi6UKe/fRCdXTlYOf87fyG+Tl3J89wk0k6BO69r0fKg7LXo2czmO0ijQ9vdcgTpuCNyxB+q4oZhUPW/bti3jxo2jc+fOTrctX76cF154gT///NPw46WmpvL++++zZMkSUlNTad26NWPGjKFOnTocP36cHj16MHbsWAYOHAjA7t27GTduHNu2bUNKyfXXX88LL7xA5cqVvRlGHirZKXkCddygxh6IYw/UcUPgjj1Qxw2Fk+x4PUFZCEF6uuvEIDs7m5ycHK8eLyoqitdff53XX3/d6baEhAT27NmTZ1uDBg2YPHmyV30oiqIoihK4vE522rVrx+eff067du2Ii4tzbL9w4QJffPFFgSw2qBQOa7aV43tPYs3KoXzV8oVySibbks2W3/8iM9VCjWbVqd64aoH34UvV85RzKZw9kkiQOYj4ulUIDnFfLdwfVc91XWfr4m1cTEylSu1K1G9ft8D7KE2Op6aQas0iXpYhlpBC6UOTJ9E4jyQCGzVACPd3kFmYOIjAho0EpIgplLgURbk2Xic7zz33HIMGDaJHjx40b96cuLg4kpKS2Lp1KyEhIXlWRFaKB0t6Fgs+/ZXFU5aTlmSvLyU0QatezRn4bH+qG0gWPMlIyeDDhz5j1+p/uPLMaFTZSO5+aRDd7nU+7ektX6qeH915jLnvz2fTwi2O+0TEhHPjA9249cm+hEbkneflj6rnVquVL0ZOZv2CjdiuOEQbGhFCn0d7MWj0bdfcR2my/Nghpu7ayj8Xzjm2VY+O4d76TelXsx7CU0JiQLBcTZg+iWAur7xtI4FMcR9Z4nYQV01Yl6mEycmEyB/RsP9NSUxk04NMbTg24fsip4qiFDyv5+wAJCYm8tVXX7FlyxaSk5OJioqidevWDB06lEqVKhVGnIWqNM/ZsaRbeGvAexzeccTpsmrNpKGZNJ775slr+hBPS0rjyTajyUzNf6Xj256+hTvHDPS5D1+qnu9eu4d37/4Am9XmXC1c07iuYQKvzHue8Cj7goL+qHputVoZ1fElzh5JzLdNm74tefqrkfneHkjn8qfu3Mr47RsR5H1Jcn8fVKcho1pdf00JT6j+PRHyv0g0BJefT3mplyxuIV17zZHwCJlMtP4wJo7maW+/jwkwk6KNxyqa+BzT1QLpNb9aoI49UMcNxaTqOUD58uV5/vnnmTlzJr/99htz5sxhzJgxJTLRKe2+eX0mR3Ycdbl+jG7TsVltfPjgp2RcwwrS7979gdtEB2Dehz9z7J8TPvfhbdVzS7qF9x/4GGuO1XW1cF3n2O7jTHt5hmObP6qeT3xqqttEB2DjL1tYNWuNz32UFlvPnmL89o2A80uS+/ucfbtYfPSAz32Y5G7C5TgAp8RFXEp3QvmZEDnPsT1CfwcTx5za2+9jA7KI0p8Gme1zXIqiFCxDyc7o0aNJTHT/Bn2106dP8+yzz/oUlFIw0pLT+eO71eh6/pmx1CWWjCxWzV7nUx8XE1M4+NdhQ22/ee17n/rwper5mrl/kpGS6XaRQN2ms2bOOlLOp/ql6rmu66ybt95Q27nj5vvUR2ny/Z4dmDwcsdEQfPeP8aKvVwvVZ+LpbVAiCJXfgpRo8jRmll1KalwT6GgkYZaFs9q8oijeM5Ts1K9f37GQ4F9//eW27Y4dO3j55Zfp168fDRo0KIgYFR/tWLHTUIkFgI2/bPKpjyVfG//g3/PnXp/6WDlzjVdVzwE2/rLZ0KkNm1Vn27IdHN193HjV8wUbjQVzld1r9xh+PTwd/SntdClZdeIINg9n2XUkuy4kcj7TtyOTISx1m7iA/QhPEIfROE6wXGXocSUaZlb4FJOiKAXP0ATlhx56iC5dujBu3DjuueceKlSoQJMmTUhISCAsLIzU1FROnTrF1q1bSUpKomvXrnz77bfUrauuLilKljSD1cIlpF/M9KmPVA+nlq5ktRr7oHfq43yq4ba5p6zSUzIxNB1N2J+nzFTj47ekG6/CfqWLZy961V7X9YBZLPBq2Tabx0TnShnWHHy7Xs746y5IR5CO/TuipwRJR0jjfxuKohQuw1dj1apVi/Hjx7N3714WLFjA+vXr2bx5M6mpqcTGxhIfH88999xDz549qVevXmHGrBgUUynGUDvNJCgbH+tTH5VrGZ+nFRru2+XCVeoaXzAyKMS+S5eNj+PQtsMu5+vkISGmYgxxlY2PP6ZCjOG2V4qvb7xunBAiYBMdgBCTifCgYDKsntft0hDEhhivWn8lSVkE5zw3BHTKolMBT4mO/XFN6KKCTzEpilLwvL70vG7dumouTgnRpHNDouIiPR590W2SLnff4FMfPR7owvSXZ6C7mRuTq13/Nj710b5/G74YOcnQKaCWPZsB0PnOjmxY4PnUXHiZcJr3aEJwSLDxquf3+nY1VrWG1xERE0F6sucr/2q3rulTH6WFEIJbatZj7r6dbo/wmISgU3w1Is1mn/qxiP6EyakuJxvnkmjk0AYpypNNF5ChgPujewIbWeIWn2JSFKXgBe5XxwAQZA6i/5N93bbRTBqVa1eiZc/mvvURFETHge09ttNMGve8cqdPffhS9bx5j6ZcVz/ebbVwgP7/18exwKA/qp7f9pSxD8Ahb93jcx+lxV11GxOkabibeSWBIQ2a+9xHlhiEJBSZz1uhfW/QydQetG8QEWSKe91OIZOYyKExVlr6HJeiKAVLJTulXJ9/9aTXsB7AVZW8hf2nXEJZXpj5rNvq4J48+snD1G2bf9V5oQlGf/c0kTHhPvfhbdVzzaTx/PfPUKHapWK1V3xi5j4PNz7QlX4jezu2+6Pqed/HetHlHvdH0R78zxBqtwjsIzsACVHRvN/5ZswmE9pVk81NQmASgjc7dKdxOd9PF+miIqnaJy4THvuaORrp4lWs4vJRyUzxL7Loc0Wb3PYCCdioQar2oefVlxVF8RufFhUsbUrzooJgry6/e+0efpu8lB0rd2LLsVGxenluerA7N9zRgbBI3+Y7XG3JtBXM/+gXx8rDpmATLXo05f53BlMuvmDKLXhb9dySnsXqOWtZPGUZpw+exRRkolGnBvR6uAeNOjVwecWWP6qeb1q4hVnv/sjxPSdA2pOpeu3qcP/bgz2W2Ai0xcbOpKfxw/5d/HJoL8lZFqJDQuhZrTYDazWganRMgfQhZCKh8kdC5Dw0LiAJJ1v0xCLuwCZqOd9BSoJZR6g+k2A2Azo2qmMRd5IlbgYR6nyfaxBor/mVAnXsgTpuKCZVz0uj0p7slEaBOm5QYw/EsQfquCFwxx6o44ZitIKyoiiKoihKSeH11VhgX/9j//79pKSkuFzLpE0b3666URQjdJvO0b0nOGXSCIkKJ7pCGY/38bbquaIYkWVL52zqdiRWYsPqEhXifCq1RNAzCWY1Gunk0BBdU2ukKaWL18nO33//zaOPPsr58+edbpNSIoRg9+7dBRKcolzJmmNl4YTFLJq0mKRTyY7tjTs3ZMAz/WjQwXl9J2+rniuKEenZ5zh24T2axqyiSpS9BpZVF2w905SwsCeoHN28aAM0Sr9IlHyOYLZcml5tvwJN2uJIF0+SranL55XSwes5O3fffTdnzpzh8ccf57rrrnO58Fnbtm0LLEB/UHN2ij9rjpVxQz5mx4qdTkcTNZOGlJIRnw+n44B2ju3eVj0vKQLlNXelOIw9NesMwnIf8eFJBGl590WrLsjWTfyT8V+qxXYusD4LZdx6ErGyP4IMp8v7c0eVKR4lU3ukYPrzUXF4zYtCoI4bCmfOjtdHdnbv3s17771Hr169vL2rovhs/se/ukx04HKJiPEjJ1GndS3KX1cuT9VzlxXfr6h6/uhHDxd6/Erpcf7iaJrFOic6AEGaRGCjesiLZNkWYzYV30S6jHzcZaID9pUaJBAmvyBL74muVfNzdIpSsLyeoBwXF4fZx9VKFcUX1mwriyYu8VjrSkrJ0mkrAe+rniuKEefTD9Ii7m+XiU4ukyaJC8nk4PmZfozMS3oiJva6XbAx97Zw+YE/IlKUQuV1snPvvfcyYcIEUlPVB4TiH/u3HCQtyXNRxSsrkntb9VxRjDiT+rOhdjZdECJXFG4w1yCMmW4TnSvZ1xFSlJLN0Gms+++/3/FvKSXbt2+nc+fO1K5dm7CwvIdphRB8/fXXBRulEtC8qUie29bbqueKYkwauhRowv2+ZdIkZi3DTzF5T8iLxtoBYC3MUBTFLwwlO1d/aLRq1Srf29QahUpBizVakVxcbutt1XNFMUITlTB5SHTAPlE50xbnh4h8YxPX4bbA1yUSkBTfeUeKYpShZGf69OmFHYei5Ktao+uIr1OZE/tPeXyD7napSKe3Vc8VxYiqcQOw2CYQFuT+aEeQJjGZB/gpKu9lcTcRfALoHuftWLjZT1EpSuHxes7O/fffz4EDB1ze9s8//9CvX79rDkpRriSEYMCz/d0mOppJo0y5aG64oyPgW9VzRfEkPDiWzUk34e4AtlUX7LlYheoxN/kvMG9pZrLo4bGivMREhhjhr6gUpdAYOrKzadMmx+mpDRs2sHHjRi5cuODUbvny5Rw7dqxgI1QUoOOAdpw7do7v/z0XzaTlOT0lNEFkbAQvzh7lWDMnt+r52wPf4/TBM5evpb10m27TnaqeK4oRdcu/xoZzZ2lXbjNWXTiuzLJJ+7fHY+nlCA6fgHCxBllxki7ewSRPEczfSC5ffXU5j9NIEZ+DFlkk8SlKQTKU7MyZM4d58+YhhEAIwRtvvOHUJjcZuuUWteKmUjj6P9GXRp0a8vuUpWxe9Bc5lmziqsTRfUgXut7Tiai4vG/KcZVjeWfJ615XPVcUd0ymYGqV/4L1FxYQImdSJ/ogJqFzPL0cp7P7U6PsfYQGlYAEQdNI4WtC9JmEya/ROHPphmCy6US6GIXUSmj5C0W5iqEVlFNTU9m9ezdSSh544AFeffVVateunaeNpmlER0dTp06dEvcBolZQLnkCddygxh6IYw/UcUPgjj1Qxw1FuIJyVFSUowTEtGnTaNSoEREREYY6UBRFURRFKUqGkp2NGzc6/i2EYNeuXW7bq6rn3su25HBi70lsVhsVqpUnumxUgfdhzbZyfO9JrFk5lK9anjLloz3e59D2wxzecZTwqDBa9GyGObToV8/2peq5Urxk5ORwJCUZiaRqVAyRAbQq+/n0g6RlHyPYFEmlyOZomsn9HWQ2mn4YmR2EkHFAjD/C9EjIREycRhKCjRogPEz0l1ZMHEJgwUYlpCjvsY8LlkzOZKZRzhpJORGKMLwUonFCJmHiBBITNmqCCCnwPpSiZyjZGTJkSL6npnLPgl15u6p6blxGaiY//e9nlk5bQUaKfUE8zaTRtm8rBj7bn4T68dfchyU9iwWf/sriKcsdKxELTdCqV3MGPtuf6k2c694smbaCWe/8kGflYqEJGnduyNOTRxAa6f9q4b5UPVeKl/OZGUz+ews/H9pDls0GQLCm0bt6HR5u3IpKESVgrouPDpz/lQg5iUaxR+DSn8+p9Gj2p/elXvknMZmuShZkBmFyCqFyDpqegrwAUWhk05VMbTg2Ucf/gwCC5N+E6V8SzFpHpXSdOCziTjLF/c7JgswhVE4nTM5E45x9E4Ic2pOpDcMqmjv1sTfpHJP+3sKq40fQL/VRxhzCwDoNeaBhc8KCrv0KSpM8QJg+ETNLEeiXxhFJlridTPEQUpTefTEQGZqzs2HDBse/T548ySuvvMLtt99O7969KV++PMnJySxbtozvv/+eN998kz59+hRq0AWtqObspCWn82b/sZzcf9q5KrdJI8gcxIuzR1G3Te18HsHzuU1LuoW3BrzH4R1HnOpEaSYNzaTx3DdP0qRLI8f2GW/O4ufPFuXbZ0RMOB9tfI/w6HCjQ71mvlQ9L61K6rn8U+mpDFv8E0mWTGxXvYYmIYgyh/Dljf2pFh2T72OU1LHvPvMFHctOREqB6Yq6Wrq0XwW15UIjris72ZHwCJlGtD4cE/scH8S5JCbARIr2CVbR2o+jgGD5B1H6KOzpytVxaVhpRor2KYhL2ZzMJkp/kmA2XrpP3vYAadq7ZIseju2bTp/gqZUL0aV02k80IagbW5bPu99CRLDvRwOD5Dai9ceBHAQ2p3HYqE6KNhkpPB/9LiwldV8vCIUxZ8dQq7Zt2zp+5s6dy9ChQ3n99ddp164dNWvWpGXLlowaNYqhQ4cyZcoUY6NRmDJmustEB+ynanKychg35COyM7N97uOb12dyZMdR15W/bTo2q40PH/yUjBT70vYH/zrsNtEBSE/O4L17/+dzTL7wVPVc6pLxIyeReOycX+NSjHtpzVKXiQ6ATUpSs7MYver3UrcK+6nU7XQoOwkBeRIdAE2AENAibid7Ev/n2B6uv+8y0QEufTjnEKU/C9J/JSmEvECU/jz2hQhdxaUTxDbC5XjHtjA5iWA2Iq5KdHLbgyRSfxFNngUgLTub51b9jlXXXe4nupTsTTrPx1v/9H0g0kKU/gyQ7ZTo5MZl4jAR+ljf+1CKHa8Xgti+fTsdOnRweVuLFi3Yu3fvNQcVCJJOJ7F+/ia35QykLklLSufP+RvzbeNOWnI6f3y3Gl1334clI4tVs9cB8M3r3xt67L0b9pOW7J83Wl+qnivFy+7ziew8f9blB1gum5QcTklmy9lTfoys8KWkT0FKe1LjTt3In7HZchAyiRB+cZlQ5LInD2mEyIUFHG3+QuQ87EdC8n8NBTqhci7ITJBZhMpZHtpLQCdE/gjAwsN7ybTmuF0oXZeSXw7tJSU7y8dxLEYj2cPzq2NmCUIm+tSHUvx4nexUqlSJFStWuLxt0aJFVK1a9VpjCgh/LdnhuW4T9nkyG3/xrerwjhU7seY4f3NxZeMv9tIK+zcfNPz4y7/1T2LhS9VzpXhZeeIwJgNLUpiEYMXxw4UfkB/Vi9rkWHgwP5qAimFpnErdTLBc5/KIgzOBWS4rmCANCJFLMVJQS5BJMJsIYhsaqQba65jlYgCWHztkpGQXObrOn6d8W8DWLFe4OM7kio5ZrvKpD6X4MTRB+UoPPvggr7/+OomJiXTv3p24uDjOnTvHokWLWLFiBR988EFhxFnqZKZZEJpweXrpSlKXZHhR9ftKhqt5S0i/aO/DZjWWHAGkJflnnpMvVc+V4iUjJ8d+EYOBU1QZOb6fti2OQoNyDLfN0VMRpOVZ0Tg/9qM7npOJgiJIM3wtlJAZIIxXSxfYv8ykefHaZ+QYf17zSnN7tOkyDUHxrVyveMfrZOfuu+/GarUyfvx4Fi68fAi1cuXKjBs3jt691fL7RsRWivGY6IB98m3ZKr5VT46pFGOonWYSlI23Vws3h5nJSjd2eLhyLf+srupL1XOleCkfFo5ucC5O+fDStYZXUlYE4aZkj6exAMLN8egiGAOF1ZFo6PhvhWMbFdE46fb0Ty5dlEdi7BJu+1Rn+zgqhkeyL/mCoX2lXJhv+4kUFZHS5PHomUBHx/Pl8UrJ4FPxlvvuu481a9bwyy+/8N1337Fo0SKWL19O3759Czq+Uqtlz2aERnh+M9BtOp3u7OhTH006N3QqoeC6D0mXu28AoPXNLQw9tinIROe7rvcpLm/lVj038rUyt+q5Urz0rF7byBkQbFLSu3rRXFJdWA5l9PA4dJuEPRerUDGyITl0RMfzOlsCnSzNf4WXs0R/j4mOxJ4UWWmOjYbYuM7Ayy7JErcC0LdGXUOJTkxIKO0q+bYsR5boa+g0oSSMbNHZpz6U4ueaKtXVqlWLFi1aUL169QIKJ3CEhIfQ9/Gb3bbRTBrVm1ajUacGPvURZA6i/5PuE1DNpFG5diVa9mwOwH1v3I3QPGcVne++Hs1PhQ59qXquFC8VwyPpXaMOmpuMVROCrgnV3V56XhIlxD5Eak4INj3/sZsEJOkP2H8RZjLFULePKTFhpRY5+OcLB0C2uAkblS9d+u6aADLFMBAaCEGGeMRDZXUTOuXJEvb3whviq1EtOsbj/K4HGjYn2ORhMcZ85NAGK/XdjkPCpTWDwnzqQyl+DH1aNWjQgO3btwNQv359GjRokO9Pw4YNCzXg0mTAM/3oMth+REW7Yq2A3AUaK9eqyOhvn7qmpKLPv3rSa1gPpz4Q9p9yCWV5YeazmILsf/hlykczavqTbhOehjfU55H3h/ocky86DmjH3S/dDlw1DlxXPVeKn+fb3EDbyvZv49oVH2a5/25ariKvte9WJLEVpuiQShzMGkea1YxNijzTlqy6QJewKvE+6pQb5NhuEQ9g4Q6APB/K0nH9Ujyp2icgfPvA94kIIUUbj07ZS3Fcfg1zY8wQD5ElBji2Z2t9yRCP5mlj/3fuWGJI1caDsK/ZFaRpfNy1D5UjonLfohxyE6A76zbinnpNrmEcGinaR9io5ojj6nFk0d+etCmlhqFFBT/99FPuuOMOKlasyCeffOKx0OfIkSMLLEB/KMpCoFJKdqzcye+Tl7JrzR5sNhuVa1Wi54Pduf729oSEuz/VZWTxJSklu9fu4bfJS9mxcie2HBsVq5fnpge7c8MdHQiLdE4QzhxJZPrLM9i27G/HpOXyVctx21O30O3eoju0e2DrIcNVz0urkrzYmE3XWXniMLP37mTX+UQkknqx5bijbiO6X1eTIA+JfUke+0XLSU4kf0WtiKWUDUnDYgtm98XmhIU9QEIZF4thSkkQGwnVZ2JmAwIrNlGVTO4gS/QtsqMOQqYSIhcQKueicQKJmRyux6Ld5XI1ZIAguYNQfRbB/IEgC51KWMTtZIlbXS7cl5GTw6+H9zJ37y6Op6VgNploUymeQXUa0apC5YIpNi0zCZELCZWzMXEE0MihNRbtLnJo73mtgEJWkvf1a1UYiwoaSnaudOLECeLjr72EQXGiqp6XPIE6blBjD8SxB+q4IXDHHqjjhiKsen6lHj16UKdOHbp370737t1p1qyZtw+hKIqiKIriN14nOxMmTGDVqlUsWrSICRMmEBcXR5cuXejevTvXX3894eH+q5ekeMeXqufeslltHN9zghxLDmUTyhJbMcbjfY7sOsbBrYcIiQixX6UW7v8io4riL5o8jUYikrBLVbb9M9HfHYvVwo6za8ixZVIlqh7VYwrhijhbGiH8iEYK2bTEZnK9Er+iFAavk50uXbrQpUsXAI4cOcIff/zBH3/8wejRo7HZbLRt25ZJkyYVeKCK73ypeu6tnKwcfv5sEb9/tZSLiSn2jQKad2/CwGf7U7tVLaf7rJq1hm/fnE1Kbnvsk7Prd6jL01+NIDI2MObgKIEhSG4gXJ9EMJdXRLdRGYsYjEXc5d/JxpekZF1k47E36VH5T26uaF+EVJew+ngNUnmY9gkFsG6a7SJleAATxxxTgcP4CmxBZHI3maanr70PRfHA6zk7V8rKymLLli2sX7+eVatWsXPnTjRNY9euXQUZY6ErzXN2fKl67q3szGzevft99mzY77IPIeCpr0bSqldzx/YfP1jA7P/8mO9jhkaE8L+N7xFd1vV6I+p8thp7SRp7iP4TEfJN7KvyXo47968lm+6kae/mm/AUxrgvWpK5mHIvTePOYLpqFUOrbr/aat7xEXSvMdT3TmwXiONmuLSuTd6q53bZdCbN9GG+D1FSX/NrFajjhiKsen6lNWvW8OGHH3LPPffQunVrHnzwQZYsWUKLFi345JNPWLdunbcPqRQib6ue+2L2e/NcJjqOPmw6Hz8y3nHE59SBM24THbAfjXrnjnE+x6QoxYUmDxEh3wZwWpQv9/JqM8sJld/5Na7tJ8e4THQAgjSJSejcUmU8J1OO+NxHDPcBNqfLyLnidzN/EGRb7XMfimKE16exHn74YYQQNGrUiHfeeYfrr7+euDjfyhkohcvbque9Hu7hdR+W9CyWfL3cfekLCdYcKytmrOLWJ/vyrcHK6kd3HuPC6STiKqkSEErJFSpnA8LDAuCSUDkDi7zHL6ezzmck0it+i8tEJ5cmIMRkZefZL6kS/W/vO7GdQ+OM23EL7Ed4IhlLMr9434eiGOT1kZ2nnnqK9u3bs2/fPl599VWee+45vvzyS7Zt2+b2Q1XxP1+qnntr97o9hmppSV2yfoG9j11r/jH8+L9NWupTXIpSXITIJQbqMIGJM5jY55eYtp6eR1iQ5/cGATSI3upTH+FMMFQ4VAAap33qQ1GM8vrIzqOPPsqjjz5KdnY2mzdv5s8//2TZsmV88sknmM1mWrVqxZdfflkYsSpe8qXqube8qTKee6rMmm28snr6xZI5l0pRcnlTOdtfVbZteornRtjX1YsMNlYY+GoaST7dT1EKg9fJTi6z2UyHDh2oWrUqCQkJxMTEsGLFClavVudeiwtfqp57K85glXGhCUf19tDIUMdVYZ5UrF7Bp7gUpbjQKYd2xZVI7tv6p8p2aJCxhWFtuiDREkWMD31YqU0Iyz22s59IK9rVipXSz+vTWBcvXuS3337jtddeo2fPntx444289957mM1m3nnnHVatWlUYcSo+8KXqubfqtq3tSGLckbp01AHrOKCtoccWmqD3Izf5FJeiFBcWcSuePswlghwao4vr/BJTh4QBnMkMx9O1uCZNcijDt3plFh65VHvKMyuqpqJSuLxOdjp06MBTTz3Fhg0b6NGjB1OmTGHdunV8/PHHDBw4kLJlyxZGnIoPfKl67i1N0xjwTD+PfZRLKEv7/vYk5+6XBmEK8rzrtevXmiCzzwcfFaVYyBK3IYlCunm7FUgytYf9FpM5KIRlp3u6Lf9k1QVH06LoeJ2PcZlM5NDaQ9VzuxR8mACtKF7wOtkZM2YMv//+OwsXLuT555+nffv2BAWpD6Tiytuq577odl9nbnvqFpd9CCGIqVCGF2aPwhwaDNhPY704+zmn6uVXqtWyBiO/+JfPMSlKcSFFLCna50gikWh5jnRITEgE6eI5coR/C+zeWONF5h62r2Js1S+nJLq0/5zJjOBI9gdEmn1f3DPVNAEr9lNmecd9+fd0xoDJP0e0lMB1TYsKlhaleVFB8K3quS/2btzP75OXsnXJdnKycih/XTlufKArXe65gfBo5zIi509eYPor37Hlt78cV43FVY6l3//18XgZvFpwS429pI1dyAuEynmEyB8d5SKy6Y5FuxObqOf2voU57lVH5hIp5tCu/CGCTTon0qPYcK4TLeIfo3x4xQLpI8z2GaF8h+DyBQ02apDGv7GZim7sxVmgjhuKSdXz0qi0JzulUaCOG9TYA3HsgTpuCNyxB+q4oZisoKwoiqIoilKSqMk2hSTx2DmSz1wkLDKUKnUro2nu88psSw4n9p7EZrVRoVr5fGtCFXfZlmy2/P4XmakWajSrTvXGVT3eJ/nsRc4dO0dQSDAJ9aoQFOx+t9RtOkf3nuCUSSMkKpzoCmUKKny/klJyKCWZ9JxsyoaGUyWy4F9zXddZd/o4iRnpJERG07qS50uO03KyOZqSjEBQNboMEcHmAo/rfGYG608dR0fSumI8lSI8zws5nppCqjWLeFmGWEI8tk+2WFh36ig2KWlRoTLxkdEFEbr/2S5g5if0FBsmWxusNPN4l7MZ6ZzJSCMsKJga0TGYPLz/oGcSzGo00smhIbpW12MfQiZi4jSSEGzUABFsdESFR9owcRCBBZ3y6KKSx7sImYSJE0hMl6rQe963iiMhU9E4CmjYqAbCeepAHlKicRSNFHRi/HYlYFFRyU4B27ZsBz9+uIC9G/Y7tlWoVp4+j/bkxqHdnJKejNRMfvrfzyydtoKMFPv5bM2k0bZvKwY+25+E+sbWwyhqGSkZfPjQZ+xa/Q9XnhmNKhvJ3S8Notu9zpMvD/51iB/en8/Wxdsd94kqG0XPh7rTb0RvzGF5P2StOVYWTljMokmLSTqV7NjeuHNDBjzTjwYd3J/7Ly50KZm3fzff/LOdE2mXF3drXLYCDzZqyQ3xnhNEj33oOv/euIpFh/dhvWJl81CTiUF1GvF/Ldo73edMehqT/t7MwsP7yLl0nxCTiVtq1OOhxi0pF+bhzdOAfUnneXXtMg6m5F1wrmpkNK+270aT8s5zRJYfO8TUXVv558I5x7bq0THcW78p/WrWQ1x1SdGRlGReWrOUfcnn82yvEhHFS207G0r4igXbMWIYjsZZhA3IgAjGE4GZdB4nyzTE6S5bz57iq7+3sOHMCce2cmHh3F23MffUb0rQ1UmPfpEo+RzBbEFcmjIsAWmLI108SbZ2i1MfQfJvwvQvCWat4z46cVjEnWSK+4smWZBWQuX3hMoZmDjj2JxNazK1YVhFG6e7mOQBwvSJmFnqqFmmE0mWuJ1M8RBS+D4x2580eYow/UtCWIggBwBJKFmiPxliGFJcdYW0lJjlQsLkVII44NhspR6ZYijZWk9/hu83hubsvPDCC8YfUAjeeecdw+3Pnz/Pu+++y6pVq8jKyqJNmzaMHj2a2rVru2yflJTE22+/zR9//AHAzTffzAsvvEB4uO9vxAU1Z2fJ18v5avR0NE2gX1kr6lIBmI4D2/H4Z484Ep605HTe7D+Wk/tPo9vynpfUTBpB5iBenD2Kum1cPxdQPM7rpiWl8WSb0WSm5r9i821P38KdYwY6ft+2bAfj7v8YqUunsQtNULtlTV6cPYqQcPsbpzXHyrghH7NjxU6u3mU1k4aUkhGfD6fjgHYFOLKCp0vJW3+u4NfD+xx1gXLZ62FLnmnZkbvqNc73MTy95rquc9+iuRy4mP8Ktm0rxfNJt8vLEhxNSeaRJfNJzc7CdtXzaxKC2NAwJt10K5UjfD/6tC3xNI8umU9+e6kAPuxyMx2qXE72pu7cyvjtG52eq9zfB9VpyKhW1zsSnr1J5xj6249OY7jSO9ffSI+qNX0ehz8I2yFiuQP7Cjx55Y7Mwh1kmMY4ti8+coBX1y4DYd/P8jwe0K5SAuM69yLYdOnKSz2JWNkfQUa+fWSKR8nUHnFsD5Z/EKWPuhSXftV9NKw0I0X7FESoD6N2zeN7nLQSqY/GzB9c/XzZL/eXpInX8yRuQXIb0frjQI5TKQ+Jho3qpGiTkaLojgYaeW83yUNE6w8jSHMxDhM6ZUnRpuQ5whWu/48wOR2JcCSr9vb23zPEI2RqjxbOoAwqsjk769ev9+rHG4899hjHjh1j4sSJzJkzh9DQUIYOHUpmpusyBE888QTHjh1j6tSpfPzxx6xZs4Y33njDqz4Lw9Gdx/jq+ekAeRMdcLxzrP1hPUu/XuHYPGXMdJeJDthP1eRk5TBuyEdkZ2YXVtgF4t27P3Cb6ADM+/Bnjv1j/7aZeiGNDx/6DN2quxy71CX7txzk+3/PdWyb//GvLhMdsD9XUpeMHzmJxGPnnG4vThYc3MOvh+31j64eiX5pywdb1rL7fKLPffxv659uEx2ADadP8P2eHfY4pGT0qsUuEx0Am5QkWTJ5ac0Sn2MCeHL5r/kmOmB/Pp5b9bujxt7Ws6cYv32j47ar2wLM2beLxUcvfzsdsewXt4kOwMtrl5JttXoXvJ/FcD+uEh24vDxhKLPBdgyA0+lpvLZuOTrSKdEB+/O1/vRxvt71l2NbGfm4y0Tnyj7C5Bdour3quZAXiNKfB3SnRMd+H50gthEuxxscZcEIld9i5g+Ei+fLHqckUr6BJo/aN0oLUfozQLbLmmUCHROHidDHFnbo10bqROrPuEx0AAQ2NM4Tqb/k2BYslxMmp1+6/eqE2P57uJxIsFxXiIEXDUPJzrJlywz/LF1qvHBjUlISCQkJvPXWWzRp0oRatWrx+OOPk5iYyL59zgXxtm7dyoYNGxg7diyNGjWiQ4cOvPnmm/z000+cOXPGRQ/+89tXSz3Oy0HAr1/8hpSSpNNJrJ+/yeWHfS6pS9KS0vlz/sYCjrbgXExM4eBfhw21/eY1e7XzP2auJseS4zJxySV1yfJv/iAzLRNrtpVFE5e4bQ/2D+2l01Yajt3fpJR8+892jwvjm4Rg1t6/fe7npwPGCq1O37UNgC1nT3EoJcltkmCTkp3nE31Own49uJdMm+cEI0fXmXlp7N/v2YHJ3ap32I+GffePPWlbc/IoKdme6zjpUvLVLt+KW/qDybY93yQkV+5t0bwIwI/7dyE9rFUsgVl7/ybHZgM9ERN7DfURLj8AIETOw34kJP9+BDqhci5I32rteU1aCZMzcLdOs7j031A5B4AQuRiNZJcJ2+X76JhZgpC+f+kobMFsIIijbovMCmwE8xcmuReAMH2G28UtwX5kK1SfUaCxFgcFPmfnwIED1KpVy1Db2NhYPvjgA8fv586dY/LkyVSqVMnlaaxNmzZRvnz5PI/ftm1bhBBs3ryZPn36+Bx3kIEVfd3Z8LP7xAUACWcOJ3Lm4Bn2bNjnuT32UzqbFm6h+72dXN6eewjP6KG8grZs+grDbff8uZegII2NP2/2mLiAfbLz7jX/EBUXaaiWlm7T2fDzRu577Q7DMfnTsdSLHElJ9tjOJiXLjx/irSDXaw25e80PJF3AYiCpADhnyUBHZ9XJI5iE8HhExCQEq04eoUlF79de+emg8Ur3vx7ex72NmrHqxBGPMelIdl1I5GKOhbn7dhruY+nRg4xsWTxPeYbrnyMMLggSxB6CgjSWHjvo8ojO1S5mZ7E7OZEOcXMN92FmM0FBGqHZSzFS/EGQSai2BavJ9XuWt9zt7yZ9H5ru+WiuwEaIXEJ20ChCslc6ncJxTSdUW0OOaaCHdoXD03t7SM5KJCa3yQ7YT2eFipVkmeIJtm3x2K9AJ5h1BJmsIAr+4gQjCuNzzetkJzk5mQ8++ICNGzeSk3P527mUkoyMDC5evMju3bu9DuSVV15h1qxZmM1mxo8f73IOzpkzZ6hcuXKebWazmZiYGE6dOuV1n7k0TRAbG+Hz/QGy0o1XBg4SIGw6mknzmPBIXZKdkeUxvujoglkY0FvZXozbZtWJjY0wXo0d0KTE5MVSUJY0z89VUTmZY6z4KYDFaiUmJtxp8u2VXL3mWanencYzRQRj1VyfLrmaEAKbSfr0/GZJ45Xus6WN8KgQj4nOlYLCg8iSxuesZeu2Yruf6OcywEC+an/N7H9TRo6aOe4XYiI0JB0MHHyx92ElNjYCPTEDD5+rDpERVkRYwT6/rvZ3mWVFGiyurokM+zguZEC2kaRNIyIsBxFRtPtJfu/tenIWGHgrFWiEhmQTFgFGD1QJJDExAqEVz7H7wutkZ+zYsfz888907tyZgwcPEhYWRvXq1dm8eTMpKSm8+eabPgXywAMPcNddd/Hdd98xYsQIZsyYQaNGjfK0yczMxGx2zjRDQkLIyjL+oXs1XZekpGT4fH+AMhXKcP7EBUNtgyJCCC0TbujIjmbSKFOhDElJridQm0wa0dFhpKRkYjPweAWtbILxWmgh4WaSktKJrRLLsb0nkVfPbXJ1n6hwzFEGd3gBMRXzf66Kmtlq/FtKbGgYycmu90l3r3lZzbuJoSLTRrTJbKhYoy4lUZrZp+c3zmw8rjLBoWSmZhEeFEyGNcdjew2BliW96iPaHFJs95MwWyXMeD5KZX/NgklKSqdcaDjnMzIMvY5huonM7CoY+auyl3UI42JSOhF6BUwcd3v6J1dqRjQ2S8E8v+72d02PxsiUeYm9ovzFpHTCrOUINnBEBHTSM8uQk100+4mn9/YQq30BBk9fVCQ2LFkxZFuDiSYIYSCTlpi5mCxAFM+x54qODiu8RQVXrVrFyJEjGT9+PHfffTeVKlXif//7H4sWLaJevXrs37/f84O4ULt2bRo3bsxbb71FQkIC33zzjVOb0NBQsrOdJ+tmZWVd09VYAFarfk0/XQd3QmjudzuhCep3qEtMxVia9WhKaITnSzR1m84Nd3TMt9/cHcFmu7b4fR73kM5oHsadq13/NlitOp3u6Ggo0YmtFEO99nWJr59AfJ3Knv+qga6DOxXJ82DkJyY4lLYV49E8zUMRgv416/n0mpcNCadcqLG/hdoxceg69Kpa29hRFAk3Va3l09gfbtzKUEwADzZsjs0muaVmPY9zdkxC0DmhGmFaMMO86OPe+k2LfH/I7ydVjjGUtAggi65YrTq31PC87ILAfsl+rahYMvQ7nep05d/HzVitOpn085joSMBGRbJszQrs+XC3v2fr1bBSL5+p3HlHYqE/VquOhb4GEh17kpepF937iaf3dovsY2gcAJmyJ1abmWxuROK+DqLERJa4BatNK7Zjz/3xhtfJTkpKCq1a2d9U6tSpw99/2ycTRkRE8NBDD7FixQrDj3X+/Hl+/vlnbLbLL5imadSqVYuzZ886ta9UqZLT9uzsbJKTk6nowzyCgtTj/q6ERoS6/eCXUjoKZoaEh9D38ZvdPqZm0qjetBqNOjUo0FgLUlBQEB0HOq/ZcjXNpHHPK3cC0PaW1lSsXt5tIVCA2566Bc2kIYRgwLP93U4X0EwaZcpFc8MdHb2K398eaNTC7dwKAYSaghhYp6HPfTzazHlNEVeevLTWTrXoGLomVHebhGkIeteoQ8Vw39YeaVS2gqFF/cqFhjsuPb+rbmOCNM1j1ewhDZoD9nHUjonz2Ee0OYSbq9cxEHURMcVhw/1aS7mFNNN5FYDeNeoQFxrm9jWUwEONWtpPjWpmsujh8bmVmMgQIwDIFjdho7LbD0sBZIphIPw3hzBTG+Z2/o09qStDlugPQA5tsFLf7TgkXFozqGimBxhhE7XIppPbCccSQRa3IkV5ADK1IY7t+bUHDYsYXODxFjWv98jY2FhSU1MBqFatGufPnycpyX7StGLFil5dFXX27FmeffZZNmzY4NiWk5PDrl27XE5ybtOmDadPn+bIkSOObbmXurds2dLboRSomApleGHmM4REhDod4cn9wH74vftp2vXy+ikDnulHl8E3ONrkyp2nUblWRUZ/+5Tnq7yK2KOfPEzdtvmvBSQ0wejvniYyxn7EIcgcxAuzRhFbKQYhyDMvJfd56DeyNzcO7ebY3nFAO+5+6fY8ba58/MjYCF6cPYpwo6e8ikjrilV4uV0XNITTUQtNCMKCgvlf194+JxUA/WrW4666+a/TAzCiWVvaVkpw/P5a+240K1fREceVMQG0qRTP821u8DkmgKm9bqNMSP5HMyOCg5l28+XJoAlR0bzf+WbMJpPTh7hJ2J+/Nzt0p3G5Co7tk2+6ze2RrVBTEFN7DbiGUfjHReagY08O86sWnsL/wGTf3yODzXzavS9lzKFOH2O5+9njzdrSq/rlv9N08Q45NHbTh0aK+By0S/uiCCFFG49OWeRVF3rnJg4Z4iGyhH+f32zRnXTxTJ447P/mUpxR9qrz4tJK60IjRfsIG9UcbS7fx37/LPrbk7ZiLk17GytNAPIkPbn/zuF60rXRju02UZ807V3A5JTs2e8TTKr2PjZRo9Bj9zevC4GOHj2aQ4cO8eGHHxIfH0/Hjh0ZNmwYDz/8MP/5z39YtGgRy5cvN/RYUkqGDRvGiRMnePvtt4mOjuaLL75g9erVzJs3j4oVK3LhwgWioqIIDQ1FSsngwYPJysri9ddfJyMjgxdffJF27doxdqzvayIUZCHQ5LMXWfbNSlZ8u4rksxcJCQ+hff823PRgd6o2THBqL6Vkx8qd/D55KbvW7MFms1G5ViV6Ptid629v71hULz/FYVHBXEumrWD+R79w7rh95VpTsIkWPZpy/zuDKRfvPLcnIzWTVTPXsHjqchKPniPIHETTro3oNexG6rd3vVz9ga2H+H3KUjYv+oscSzZxVeLoPqQLXe/pRFRcyVjxFODgxQvM2beLJUcOkGHNITY0jP416zGgdkOPKxUbfc3/PHmMT7dtYH/yeST2b91NylXkyRbtaVzO+UioVddZduwgs/fuZE/SOQSChmXLM6hOI7omVPdccsCAbKuVL3Zs5KcDe0jLsZ+SDg8Kpm/Nuoxs1o7QIOdphGfS0/hh/y5+ObSX5CwL0SEh9KxWm4G1GlA1OsblOCbu2MQP+3c7LkUPNQVxc/Xa/F/z9kS6mPdXLNlshPMuofyMfU0Y+we4lYak8C6YnFeCvphlYf7BPczbv5szGWmEmILoHF+NO+o2omHZCk7tAUL0mYTJr9EcKw8Hk00n0sUopOa8nwiZSohcQKici8YJJGZyuB6LdhdW0bygRu9gdH83yX8I1WdiZvmlchEVsIgBZInbkCLW+Q4ykxC5kFA5GxNHAI0cWmPR7iKH9uDhFGphM/zeLnMwyyWEypkEsRcQ5NCYLO0usukCwvkIliaPESrnECJ/RZB66chXXyxiELqoUniDMqhYVD0/ceIEQ4YMoUqVKnzzzTdMmTKF//znP0RHR5OamsqIESMYOXKk4cdLTU3l/fffZ8mSJaSmptK6dWvGjBlDnTp1OH78OD169GDs2LEMHGj/xnf+/HneeOMNVq1aRUhIiGMF5RA33xg9UVXPS55AHTeosQfi2AN13BC4Yw/UcUMxSXYALBYLhw8fpn79+gAsWLCALVu20LRpUwYMKP6HiK+mkp2SJ1DHDWrsgTj2QB03BO7YA3XcUDjJjk+LCoaGhlK/fn2ysrJITU3l5ptvpl+/fr48lHJJaal6rhQvUkqOpF4kNTuL2JAwEqI8TxL2R9VzX3hb9dxitXIoJQkpJQlRZYg2e77PybRUzlsyiAg2UyM6xu06RwDIrEtVtm3YSECKGI99nElP42xmOuFBwdQoE+vx6jxfeF31XDFO6pg4hCADnXLoorLn+yhFzqdkZ/ny5Xz++efs3GmvVWQymWjVqhVPPvlkkU8ULmlKS9VzpXiRUvLzob1M370tz6rN9ePKMbRhC7pd5zwB0R9Vz33hbdXz5CwLX/29hfkH/yHzUg2sIKFxU7VaDGvcymXCt+bkUb76ewt/n798tWd8ZDT31m/KgNoNnBISIVMJk5MJkT+iYV8sUmIimx5kasNdTvDcePoEX+3cwpazlxdArRQeyd31mnBn3UYFkpB4XfVcMU7qhMg5hMlvMHH5+c2hGZnaMHJE8b4SNNB5fRpr4cKFPPPMM9SvX5+bbrqJsmXLkpiYyO+//87BgweZOnUqrVu3Lqx4C0VRncYqLVXPi0Kgjhs8j11KybjNa5izb1e+1cIfa9qGoY1aOLb7o+q5L7yten4uM4NHFv/EmYw0l+MICwrmix79qBN7ecL8rL1/8/7mtY6q81f30ad6HV5p39WR8AiZTLT+MCaOuqj8bQLMpGjjsYomju3zD/zDvzf8gSaEy6UHuiZU553rb8w34TGyv3td9byEKBZ/61InUn8FM4vAqVq4fc9JE2PI0gquVE2xGHcRKbKq51f6/PPP6dWrFz/++COPP/44d911FyNHjmT+/Pl069aN999/39uHDFilpeq5UrwsOXqQOft2AflXCx+/fSNbzp60b/NT1XNv+VL1/I0/l7tMdMA+jgxrDqP++A3bpSNXuy8k8v7mtQB5Ep0r+/j18D7mX1FcNUJ/BxPH8qn8bQOyiNKfBmn/uz2ckszYDavsfeTz3XLl8cOOAqi+8KXquWJciJyLmUUIXFULt+8HEfI/joKbSvHjdbJz9OhRBg0a5PK2O++806e6WIGotFQ9V4qfGf9sx/1SfPajHDP32D9c/VH13BfeVj0/nJLMhtMn3I5Dl5LTGWmsPXUMgNl7d3rsQwAz9uxASokmT2NmmYdK0zoaSZjlUgDm7tvp8SpmiX28uUmYt7yueq4YJyVh8hs8L+GuEarP8kdEig+8TnZq1arFjh07XN526NAhEhKc15JRnP21ZIfhqucbf9nsh4iU0uCCJZNdFxKdjlJczSYlf5w4gk3XWXn8sMcPfLAnSCtPHC6gSN3TpfSq6vn5zAxWHT9saLKvSQhWHj8M2OcDeepDAkdSkjmelkKwXGUofomGmRWA/UibkXIcZzLS2Z9srL7e1ZYcNV71/Mp5SYpnGocv1QRz//wKbJjx79FPxTivJyi//vrrPProowD079+fChUqkJyczLJly/j44495/fXXOXnypKN9lSpFv0BRcZSZZkFowmONKKlLMlINlCdWFCA9x/gpT11KsnWboWKbYJ+pkJ5jrO21yrbZvKp6nmHNId2a4zTvxhVdSsfzlGlw7AAZOTkI0rF/R3R/dESgI2Sa93140dbX+/nrNSwtNIzP5xRGSskrRcLrZOfOO+31jT766CM+/vhjx/bcec7PPfdcnvbqtJZrsZViDBXD1EwaZat4rvejKGCvlm7kAx8gLCiIUFOQ4ausdCTl/XRFVojJ5FXV89iQMMqHRWCTno+WakJQLiwCgLjQcM5bXFeXv1rZsHB0KuAp0QH7RGVd2FcsLhcazrG0FEN9+HrFW/mwCJIsmYYKiPrrNSwtdMp70dZ5pXilePA62XnnnXc8rz2heNSyZzNCI0KwpGe5bafbdDrdqS5pVIyJDDbTJaE6f5w47PbIiEkIx2XbvavXYcrOrZ4fXJKntlJhEkJwS816zN230+M4OsVXI9JspkfVmnyweS1WDwmPTUr61rCXI+lXsy7Tdm9zewpIE4LWFapQLiycbNkFZChgcR8/NrKEvehvv1r1Gb9tg9tERAANy1bguqgybh83P/1q1uP9zefcthHYi6XWjVUfyN7QRUVyaEUQW91WfZdoZIlb/RiZ4g2vk53csg3Ktcmtej73vz/l20YzaVRtdF2xrnquFD9DGjZzO7dGAEGaxp2XioXmVj3/48SRfD/0r7XquS/uqtuYnw7sRrfZ8k0Urqx6HhMSysA6DZi9d2e+7TUhaFWhCvXjygEwsE5DZu3dicWak+/HmC4lD+Repi8iyBT3EiYn5ztdVWLCSgOs2Nccu7VWfb7ZvY20nOx8n18JPHjFUgDe6l2jDlN2biEpy+K2D0fVc8UrmdpDROkj8r3dXlk9HItQn4/FlU8rTGVnZzNjxgxGjhzJXXfdxYEDB/juu+/Yvn17QcdXqpWWqudK8dKobAXe7NDdURn8SpoQmE0mxnXulecogj+qnnvLl6rnT7boQNcE+4J+V94n91/1Ysvxzg03OrZXDI/kf117ExoU7LIPDcFLbTvTuuLluYeZ4l9k0Qe4usq2fQqrjRqkah86CknGhITycbc+RAabna6SMwmBAJ5t1ZFO8dW8e4Ku4EvVc8W4HNGedPHSpdfYuVq4JJxU7VOkMH7KS/EvrxcVvHDhAg888AAHDx6kZs2a7N+/nzlz5vDJJ5+wbt06pk6dSosWvn9DKQpFWRurNFU996dAHTcYH/vRlGTm7NvF70f2k5aTTZmQUG6pUZeBtRtSMcL5CI0/qp77wtuq57qUrD5xhNn7drI98QwSSa0ycdxRtxE3Vq2F2cWieucyM5i3fzfzD/7DBUsm4UHB3FitFoPqNKRmGRdz5qQkmHWE6jMJZjOgY6M6FnEnWeJmEKFOd7lgyWT+gX+Yd+AfzmWmExoURLeEGtxRtxF1Y8u5fQ6Mvua+VD0v7orT37pJ7idUzsIsFyPIRKcsWeJWLGIgUrh/Db1VnMbtb8WiEOiYMWPYsGEDU6ZMIT4+nsaNGzN37lxq167Nww8/THBwMFOmTPHmIYucKgRa8gTquEGNPRDHHqjjhsAde6COG4rJCsrLly/nySefpFq1annO/YaEhPDQQw+xc+dObx9SURRFURSl0Hg9QTkrK4uYmBiXt5lMJnLUGg6KUmz4UvX8TEYaG0+fQEPQplI85cMjPPZxLC2Fi1kWyoSEcl1kdLGYBJtssbDu1FFsUtKiQmXiIz2P3duq51k2K4cuJmOTOgmR0ZQJcT59dTWTvoMg9qNThhw6g+b+bTjbZmN/ygXCslKIlsFEBXmu3u4P5zIzOJ2ehtlkomaZWFVk1A0hL6JxHNCwUR1EWFGHBFKicRSNFHRi0MV1RR1RofI62WnSpAkzZsygS5cuTrctWLCAxo0bF0hgiqL4zpeq53+fO8Mb65Zz9Ko1YWpGx/JGx24u55X8dng/03b9xf6Ll1f+rV0mjvsbNi+yybBHUpJ5ac1S9iWfz7O9SkSUfbJxpXin+3hb9Tw1O4spO7cy78BuxyJ9JiHofl1NHm7ckhplYp36CNGnES4nO6qkg31ya7atM2liLGjmPO0zcnL4etdWfti/m5Rs+xIVGoIuCdV5uHHLPMVM/Wnn+bNM2rGZdaeOOa56iw0J4466jbivQVNCTF5/rJRamjxKuD4RM785SozYr9oaQKZ4GCl8W2rgmkiJWS4kTE4liMt15azUI1MMJVvr6f+Y/MDrOTubNm1i6NCh1KpViy5dujBx4kSGDh3KoUOHWL16NZMmTaJ9+/aFFW+hUHN2Sp5AHTcUTtXzP08e46mVC/O/ZBv44sb+NCtfybHt820b+HrXX/n2cX+D5oxo3tbHUbrmaex7k84x9Lcf3a7N8871N9Kjak3H795WPb+YZeGRJfM5lnrR6TJv06Wr3T7tdkueq8QibK8TwgLH4+bKvbdOeZLFfEfCk5aTzWNLFrD/4gWXfZiExv+69qZVRf+uUL/6xBFGr/odievK6s3KV+Kjrn0IDSq4hKek/q2b5D9E68MRWJxqqUlM6FTmovYVUrhOWgtr3OH6/wiT05FO1dvtv2eIR8jUHi2w/nxRLObstG7dmilTphAWFsakSZOQUjJ16lQSExOZMGFCiUt0FKW08bbqua7rjFr1m9tF73TgyeW/On5ffeKoo4J2fn1M2/0Xq08c8WEEvhux7BePZSZeXruUbKsV8K3q+bsbV3HcRaID9gULs2w2Rv2xiOxLBTeD9NUuE53c3wWgkUikfN6x/cPNa10mOrl95Og2Rq/6nQw/Thu4YMnkhdVL0GX+ldW3J55hwo5Nfoup2JJWovSnEWS6LBorsKFxikj9Db+GFSyXEyanX4rh6mTV/nu4nEiwXOfXuPzBp5Osbdq04fvvv2fLli2sXLmSTZs2MXfuXK6//vqCjk9RFC95W/V85t6/yTFQbTvTZuXXg3sBe4VuT0U3NSH4/lIf/rDm5FHH6R53dCn5apd9xWhvq56fSU/zWDxUl5KkLAvLjx0CIEJ+6ngcd8ysBj2b5CwLCw/vc7uqs8R+9GfRkX0eHrXgzD/wDzl6/gs8gj1h/HH/Lq/qgZVGZv7AxFm3Ky7bC4euQZPH/BZXmD4D6eFjX6IRqs/wU0T+41Oyk5aWxpkzZwgNDSU2NpbvvvuOt99+m40bNxZ0fIqieMGXque/HDL+gfnjgd1kWnPYeOaExyrbupRsPHPCb0cf5uw1fiXokqMHAe+rnq8+edTQ42tcrqxuYr/HRMd+hEfHzDLWnTxmqAiqAFYcO2wonoKw7NghQ7W3Mq1WNp85VejxFGdmucJp8UFXJBpmudIPEYGQqQSzxW0CBvb9MJh1II0XFS4JvE52tm/fTvfu3Zk+3X4o7O2332bcuHHMnz+fBx54gKVLlxZ4kIqiGONL1XOLzWr4PplWq9fJi7++5XszdsulmLytep6ek+3xiBbYj3CkOeIxPi1SkEy61dg4JPaJ0v7izfObYXAMpZWQ6RgpGGtPi40Vor1W3vQjkKWugrvXyc6HH35IzZo1ueuuu7BYLCxYsIDBgwezYcMGBg0axBdffFEYcSqKYkBu1XMjcquexxm4XDpXubAwos0hBBu8zDhY04gy++dS6Qpe1O2KCbFf+hsXarwCeNmw8EuV1T0nLyYhrqguHmQ43bFRk/Jh7i/1z6UJ4ddaZRXCIwzvW0bHUFrpojwYOLIDNnQKduXl/OiUQRq8AFtiRlK6XkOvk51t27bx2GOPcd1117Fu3TosFgu33mqv9NqnTx/27fPfOWRFUfLKrXruaR7KlVXPvSlA+UiT1gSbTPSqVttQHz2r1XZZnqEwPNKkleG29zVoCtirnhuZe9S2YjzlwsLpnFCNUAOXVtukpG9Ne2X1HNp4bC8BnTCsWls6VL6OqGCzx/voUnLLpT78oV/Neh5PjwJUDI/Ic9VeIMoSfV1OTHYWRLa40XOzgiBCyeZGj6fXJCayxC0gStcSAl4nO5qmYTbb/xBXrlxJdHQ0TZva3zjS0tIIDTX+LVFRlII3pGEztx9JV1c971ClKuUMHOGIj4ym0aX6SoPrN0V4+JYvEAyu39Ro2NesWnQMtWNc1LG6SrQ5hJur1wHsVc9DTUFu3wivrHoeEWzmnvpN3D6+SQgalS1Pi/KVAUgXowD3J7MEYBGDATCbTNzfsLnHPmqWiaVDZf8tBNejak0qhUd6THIfatTS0Km+0sxKY3Jo6TaxkAgs3IUUnhe7LCiZ2hBH3/nFBJpjXyxNvE52GjduzJw5c9i6dSsLFy6ka9euCCE4f/48EydOVIsKKkoR86Xq+bSbBxIRHJzvY5YxhzC1122O32vFxPFup5sI1jSnDzZNCII1jXc73WQo+ShIk2+6zW3iFmoKYmqvAY7ffal6/kjjVvS+lCxd+fzm/qt6dAzjOt/sWH1Z16qTJt4C7AnPlUlP7u9Z9CBTe9yxfUiDZtxeu2G+fVSJjOZ/XXv7tThriCmIT7v3pWxouOOS+Vy5MQ5t2Jxba9X3W0zFlhCkav/Fhn0/uTK5yE2AsulBhvZ/fg3LJuqTpr0LmFxWb4dgUrX3sQnnRUdLOq8XFdy1axfDhg3jwoULxMXFMWPGDKpXr06HDh3QdZ3JkyeXuIRHLSpY8gTquKHwqp5brFY+3baeXw7uJePSxN3IYDP9atbj8aZtMLtYKO5kWgpz9+3i18P7SM3OIsocQu/qdRhUpyFVDJRn8JaRsVt1nYk7NuVZeTjUFMTN1Wvzf83bE2l2PkXkbdVzKSV/njrO7H072XL2JDYpqR4dwx11GtGzWm2Xi+pp+gEi5PsEs4ncyas2qpEp/kW21stlH5vPnGTWvp1sPH0Cm9S5LqoMt9duSO8adQgLyj85LUxp2dn8fGgPP+zbxcn0VMwmEx0rV+WOuo0K5fRVif5bl1mEyN8JlbMwcQDQyKE5Fu1OcrgBRP7JamGOW5PHCJVzCJG/IkhFUoYs0ReLGIQu/LtQpSvFouo52E9XHThwgDp16hAebv8W9dtvv9GyZUvKly/v7cMVOZXslDyBOm5QYw/EsQfquCFwxx6o44bCSXZ8moEUGRlJs2bN8mzr1cv5m4miKIqiKEpRK13TrRWlgOhScuhiEhnWHMqHRVDJxWmfksCXqufeslqtfLtnB4dSkqgRHcu99ZoQVIC1kXIdT00h1ZpFvCxDLMWj8rcvVc81eRqNRCRh2Kjp9lSGoigFQyU7inIFm64ze99OvvtnB6czLlenblmhMg82akHbSglFGJ1xvlQ998XgX2dz4GJSnm2fb99IzehYvut7R4H0sfzYIabu2so/F845tlWPjuHe+k0dl8/7my9Vz4PkBsL1SQSz2bHNRmUsYjAWcRcI/1yiryiByKc5O6WNmrNT8hTGuG26zktrlrL8+CGn2zQh0KXkpbad6V/EV5sURtVzX3Sd/RWZ1vxXXw4zBbHizoeuqY+pO7cyfvvGfMcxqE5DRrW63q8Jjy9Vz0P0n4iQb8Kl2uq5cu+dTXf7VTL5JDyB+ncOgTv2QB03FJOq54pSWs3a+7fLRAdwfKiN3bCKQ1cdyShuvK167ot/LZnvNtEBe+HQfy35yec+tp49xfjt9np7+Y1jzr5dLD56wOc+fOFt1XNNHiJCvg3gVJco9xJuM8sJld8VduiKErBUsqMo2I/qfLdnh8d2QsDcS4lEceVt1XNf/JV42mC7Mz738f2eHR4XsNMQfPeP59etoPhS9TxUzgaPSzBKQuUMkEZW3VUUxVsq2VEU4MDFC5zJ8Hwq0yYlS48d9ENEvvGl6rm3DiZf8Kr9vqRznhtdRZeSVSeOeKxDpSPZdSGR85n+KaboS9XzELnEY+kAAZg4gwlVbkdRCoNKdhQFHJNMjfC26rc/+VL13FtXTng24mjKRa/7yLbZDBXczJXhx8rq3lY9967atH+SNkUJNCrZURS8q9JcLsx4pWx/86XqubeaXDHx1ohGZSt63UeIyUS4wRWCNQSxl6qYFzZfqp7rlDNc9Vyn5C3KqiglgUp2FAVIiLIXufSUJghEkV+N5Y4vVc+9Vc5AMcgr+6kU6f0aRUIIbqlZz9A4OidUc1kCojD4UvXcIm4FjzN2BDk0Rhf+K+ypKIFEJTuKcslDjVq4/QauCUGU2Uz/mvX8FpMvvK167ovB9YxVM7+7nvsK4e7cVbcxQZr741QSGNKguc99eMuXqudZ4jYkUZcKLbomkGRqDxdorIqiXKaSHUW55Ib4aoxq1dE+WfTqSt4IIoPNfNytD7Gh/jll4itfqp57a2SLdjQt5/70VNNyFXmiRXuf+0iIiub9zjdjNplcViQ3CcGbHbrnWc/GH7ytei5FLCna50gikWhXVT03IRGki+fIEZ39NAJFCTxqUUHUooIlUWGOe2/Seebs3cny44fItOZQLiycW2vV59ZaDYgrBolOYVU998XMPTv4fNtGLLbLa+6EmoJ4vFkb7rqGozpXOpOexg/7d/HLob0kZ1mIDgmhZ7XaDKzVgKrRMQXSh7d8qXou5AVC5TxC5I+OchHZdMei3YlNuD9aGKh/5xC4Yw/UcUMxqnpe2qhkp+QJ1HGDGnsgjj1Qxw2BO/ZAHTeoFZQVRVEURVG8pgqBKkoJkpKdxankVMrkhFNWhBBMwRePTMvJ5mhKMgJB1egyRAS7v9JJSsmxtBQuZlkoExLKdZHRRVKc0zmwTEwcBnR0rkOKgq/4rigllpRoHEUjBZ2YUn8loEp2FKUEOJGWwqQdm/n9yAGs0n5YNywoiH416/Nw45bEhIRecx9n0tOY9PdmFh7eR86llZVDTCZuqVGPhxq3dLm+0G+H9zNt11/sv3h5VeXaZeK4v2FzelWvfc0x+ULIJMLkZELlPASZAEiCyKIXmdojpf5NXVHckhKzXEiYnEoQl+vKWalHphhKttazCIMrPGrODmrOTkkUSOPen3zhUuHNHKcF7UxCUCE8gkk33XZNix0eTUnmkSXzSc3OctlHbGgYk266lcoRUY7tn2/bwNe7/sq3Ivn9DZozonlbn2NyxdPrLmQiZfSH0DjjVKLBfuVTGCnaJGyiToHGVdgCaX+/WqCOvbDGHa7/jzA5HYlAXPGXm/t7hniETO3RAuvPF2rOjqIEGJuu8+zKRWS4SHTAvnjd2Yx0Xl+33Oc+pJSMXrXYZaKT20eSJZOX1ixxbFt94ihf7/rLfv+rH+/S/6ft/ovVJ474HJcvIvXXXCY6AAIbggyi9KdVwU0lIAXL5YTJ6QB5Ep0rfw+XEwmW6/weW2FTyY6iFGNrTx3jdEYaupsDsDYp2XjmBIe9rFmVa8vZUxxKSXJbBsEmJTvPJ7L7fCJgr0juqUaUJgTfX0NldW9p8hBm1rstuinQMXGKYNb4LS5FKS7C9BluF7cEkGiE6jP8FJH/qGRHUYqxlccPGyrNoAnBH5eqbBdWHyYhWHniMJnWHDaeOeE2AQN7odGNZ074rXCqWf7h8Y0c7KezzHJF4QekKMWIkKkEswWB+1NiAp1g1oE0XlS4JFDJjqIUYxk5OR6TCrCv8Oxr5W+j9xMI0nNyvE5eMv1UkVyQjrG3NB0h0wo7HEUpVgQZXrSVjsn9pYVKdhSlGCsXFu7xdBGATeo+T1A2ej8dSfmwcKLNIQRrxt46gjWNKHOIT3F5y14x3MhcHA1dqOriSmDRKYM0eAG2xIwkopAj8i+V7ChKMdanRl23c2lymYTGjVVr+dRH7+p1DPWBhF7VaxNsMtGrWm1DFcl7VquN2VTwawG5ki1uBAPrDglsZIlbCj8gRSlORCjZ3Ij08DciMdn/PkTpWplGJTuKUozVjytH20rxbo/uCGBgnQY+r7VTLTqGrgnV3fahIehdow4Vw+01tQbXb4pwW4/cftprcH1j1dELghSxWBiEdBOXxEQ2bbGJBn6LS1GKi0xtCEC+fyP27RoWMdiPUfmHSnYUpZh75/obqR9bDiDPW1RuctIloTpPtuhwTX281r4bzS5VMb8y6cn9d5tK8Tzf5gbH9loxcbzb6SaCNc0pSdKEIFjTeLfTTdSOibumuLyVoT1FNt0A8kxWzn1zt1GPNO0/fo1JUYoLm6hPmvYuYHI6wmP/ewkmVXsfm6hRJPEVJrWoIGpRwZIo0MadbbOx5OgBZu/dyYGLF9CERtNyFRlUpyE3xFczNK/HE6uus+zYQWbv3cmepHMIBA3LlmdQnUZ0TaiOycU8nZNpKczdt4tfD+8jNTuLKHMIvavXYVCdhlSJLPjyDIZed6kTzCpC9VkEsw3QsVIbi7iTbNEThPvyF8VRoO3vVwrUsRfmuDV5jFA5hxD5K4JUJGXIEn2xiEHookqB9uULVfW8kKhkp+QJ1HGDGnsgjj1Qxw2BO/ZAHTeoFZQVRVEURVG8VrqmWysl0vnMDE6lp2E2adQsE0eQwcuavZFkyeREWirBmkaNMrF+u0KooHlb9VxKyZHUi6RmZxEbEkZClOdTS95WPQ9kKdkW1p48Ro6u07xcRa6LjinqkHwjczBxCEEWNiojRbmijkhRCpRKdpQis/t8IpP/3szqk0cdVVpiQkK5o04j7mvQjNCga9899yWdZ/LfW1h5/DD6pV6izCHcXrsBDzRsQXhw8DX34Q/eVj2XUvLzob1M372NI1eUkagfV46hDVvQ7TrnCYi+VD0PVKfSUnlhzWJ2XziXZ3vF8AhGt76BG+KrFVFkXpIWwuQ0QuUsNJLsmxDkcAMZ2nBsomERB6goBaPI5+wkJyfzwQcfsGLFCtLS0qhXrx7PPvssrVu3dtn+xx9/ZMyYMU7bf//9d6pV8+0NRs3Z8b91J48x6o/f0JFOKwRrlybGftb9lnwTHiPj3nL2JE8uX4hN6k7ryGhCULtMHONv7EdkMT9y4W3Vcykl4zavYc6+XflWJH+saRuGNmrh2O5L1fOiUBz292Mpydy9cA5WPf/+X2rbmf616hdYn4UybplJtP44QfztVELAfqWOIFX7gBxxfcH056Pi8JoXhUAdN5TSOTvPPPMM27Zt44MPPmDOnDk0atSIhx9+mAMHDrhsv2fPHtq2bcvq1avz/CQkJPg5csVXF7MsPL96MTapuyyFoCPZdT6Rz7Zt8LmPjJwcnvvjd6y6c6ID9rpN+y9e4IPNa33uwx98qXq+5OhB5uzbBeRfkXz89o1sOXvSvs2HqueB7LFlP7tNdADe2fAHadnFu7ZQuPzMZaIDXCqmaiNKH42QyX6PTVEKWpEmO0eOHGHNmjW89tprtG7dmpo1a/LSSy9RsWJFfv75Z5f32bt3L/Xr16d8+fJ5fkwldA5GIPr54F6ybVanD+Ir6Uh+OrCb9BzfPjB+O7KftJxsx6krl31IyaLD+0jOsvjUhz/4UvV8xj/b0Tws+GcSgpmXKpL7UvU8UP197gyJmZ5rDElg/DUk64VOphMqf3BbFFIggSxC5Hz/xaUohaRI5+zExsby5Zdf0rhxY8c2IQRSSi5evOjyPnv27KFXr14FHktQUJEf5PJJ7iE8o4fyioPlxw+5TXRyZdlsbEk8RbeqzvNLPI17xfFDTqdwXLFJyfoz/9/evcdFVad/AP+cGUAYdAQFwcRUFLwAIiikaSqSZqKW/rIL4WWz9LWpbWmroZVaW1qZ2Aqampddy9bMW1ZakmVeUdQwNUURDERRbgJyn/n+/nCdZeQ2IHM783m/Xr7CM1/OeZ55sHk453vONwMR3r4GRGR6B65egVKS6l3OQSFJOJh5Ba2cnHAut/6GRCMEfr16BZICOJBp2DGUkoQDmVcQ4OHRoByakrl/3jcnnzF47K+ZVxBtN7BJjtvUedtpEiFpywwYKdAM+1BpN6lJjtsY5q65udhq3oBxcjdrs6NWqzFo0CC9bbt378aff/6JAQMGVBufm5uL7OxsHD9+HBs3bkR+fj4CAwPx+uuvo1Onxj/xUaGQ4Opq3YueqdVO5g7BYMUaw1fBlhwUddamtrxLtRqDGioJgLC33PpXSFpoDUhEIUnQ2gFKleFnOLVCQKV2RKWirgUW/keSJGiUwiLeK3P9vJcJQxYavaNcq2ny96qp8hYllRA1/z6pRwJgp7ht0zU3N1vNG2ja3C3qbqwTJ05g7ty5CA8Px5AhQ6q9npycDABQKpX44IMPUFxcjBUrViAyMhK7du2Cm1vjbpfUagUKCuo/NW2JlEoF1GonFBSUQKOxjklsbRxVSEVenZeY7lIJJfLyqk8ery/v1s2c7jQA9ZytEACchV2Nx7AELe2aQSEBmnreKo1Wi+aSPZRldyZ4G/Te2tmjtLAMaqWDQY2hVgi0UDiY9b0y98+7WzPD70hrYd9071VT563UtkRzA8YJKFCpdUexDdfcXGw1b8Dw3NVqJ4PP/lhMsxMfH4/XX38dgYGBWLp0aY1j+vbti2PHjqFly5a6bXFxcQgLC8O2bdswZcqURh/f2me7azRaq8nh8Y6+OHIto95xbk4qBLb2rDOv2vIe0dEHP/15ud5jtLB3wEMeXhb73g3v4IMvz/9e7zilpMAQL284KuwwyKsjfr2aVudlKaUkYaS3LzQagcce7IK1v5+sPxgBDH2ws0W8V+b6ef9LjyBs/e/k7/o85ePX5DE2Vd6VIghOcIcCN+s8qydBi1KMtumam5ut5g00be4WcTHw888/x4wZMzBw4ECsWbMGjo61r95ctdEBAJVKBS8vL2RlZRk7TGoiQ9p3gldzNZT1rOf0F7+gGtdjMkS/tu3RuaVrvceY0KOXRT9gsDGrno/vEVjnmZo7lyYUeNr3zly5xqx6bqvcVc7o6Vb/nCUnpR2e9vEzQUSNJClRIr1QZ6MjoIQG7VAuVT/LTmRtzN7sbNq0Ce+++y6ef/55LFu2DA4OtT/zZNOmTXjooYdQWvq/u2eKioqQlpaGLl26mCJcagL2SiWWh42Au5MzJOiv5H23ORnfPRD/16XxDzRTKhRYNniEbjHKmo4xtkt3jO8e2OhjmEpDVz33a90G7/QbAqUkVWv2FJIEB6USSwY+hvYt/veLQ0NXPbdlcWEReKCO5w05KBRY99iTUBjhSeBNqUwahxJpIgDorYAtIEFAghbuKFCssMqFU4nuZdaHCqampmLUqFEYPHgw5s+fr/eao6MjVCoVcnNz0aJFCzg6OuLatWt44okn0LdvX8yYMQOlpaVYunQpMjIysGvXrjrPCNWFDxU0j6KKcnyfmoytF88hs6gQ9koF+np6YZyvP4LatK3zew3Nu6SyArtTL+Lri2eRXlgAO4UCfTwewNO+/ujj8QCkJlgt3BQas+r5nwX5+PriOfz439vwWzZzxMhOvhjbpQc8nKufoWnMquemZik/71qtFhvO/Yavks8g77+PLmimVOLRB73xt6B+aNmscf8vqo0x87YTp+Co3Qx7HIaECmjwAMqkp1AmjYKQzH8mz1Jqbmq2mjcgw1XPP/30U8TExNT42pgxYzB9+nSEh4dj0aJFGDt2LADgjz/+wJIlS5CUlAQhBPr374/o6Gi0bVv3h2Nd2OxYH1vNG2Dutpi7reYN2G7utpo3IMNmx1Kw2bE+tpo3wNxtMXdbzRuw3dxtNW/AOM2OxdyNRbbLFKuey8XVogKczr6OFi2cENiyDVrYNTN3SEREFo/NDpmNKVY9l4sTWZl4L2E/rt4u1Nvu49Ia7/UPRwe1i3kCIyKyAvwVmsziSGY6Xty7E4eupevdJp1fVoq1Z05i2r5vUVpZabb4LMm+9FS8vO/bao0OAFzMz8Fz329Bcl62GSIjIrIObHbI5Eyx6rlclFdW1rvauEYITNv3nYkiIiKyPmx2yORMseq5XKw7d6reJS8AoKC8DIcy/zRBRERE1ofNDpncvvTLBq96npiVafR4LFm8AUte3LWlAStyExHZEjY7ZHJFDThbY+tndsoaMG/pdoXhq8kTEdkSNjtkch6q5lDUuSrP/7g7ORs5GsvWkCfxtlHZ9ntFRFQbNjtkchGdfKE14EKWm5MKwfUsGyF3Ud17Gjx2SkAfI0ZCRGS92OyQyZli1XO5GN7RB2qH+h8c2MWlFZ+1Q0RUC9v+JCGzMMWq53Ly78fGwlFZ+wMW3RxVWDv0SdMFRERkZfiIWjKLB5qr8cWIpxq96rktadu8Bb57MgrLfzuKPWmXUKq5M2lZ7dAMY7t0x0sBfbjEBhFRHdjskNk0t3fA077+eNrX39yhWLzmDg6IDh2I6NCBNr1AIBFRY/DXQSIiIpI1ntmxYjlXc3HrRj7atHWBum0rc4djMhqtFql5ebAvvQWVVolWDipzh2QyBeVluJZfiJYVKrSWmsEeSnOHZNPKNJVIvZUPjdDCq7m6QY8KICLTYbNjhc4e/APbl+7CuUPnddtat2uF4VOGYviLj0JpJ88PwEqtFl+eP43NyWdws6RYtz3Uox1e8A+W9Tyfq0UF+Oz3E/jxSgoqxZ1LV052dhjl3Q2T/YPhwg9ZkyosL8P6s6ewI+UP3cMclZKEIe29Mdk/GJ1aupo5QiKqShLCgIV3ZE6j0SI397a5wzDIgS2H8emMtZAUErSae+ZrSEDwsF54bd002TU8lVotZv26BwnXMqo9oUchSYAAFj4chmEdupglPmO6lJ+LqfHfoKSyApp7/rkqJQltVM74bOiTcHOS/xkuS5ivdKusFC/Ff4P0wlvV1i1TShIclErEho2Ev1ubJjumJeRtLraau63mDRiee6tWzlAqDZuNwzk7VuR6ahZW/W0dhBDVGx0AEMDJH3/Ddyt/MH1wRrbh3KkaGx0A0AoBLQQWHPkF124Xmjw2Y9JotZi1fw+Ka2h0gDsrnt8ovo0FR342Q3S2afHxA8ioodEB7tSjTKPB67/uQblGY4boiKgmbHasSPwGAz7QBLBnzV5oKuXzP9oKjQZfXThT7zOXBQS2X/rDJDGZyuFr6bheXFTnyucaIXA86yrSCvJNF5iNyrpdhJ/TU2tsPO/SCoG8slL8nJ5qwsiIqC5sdqxIwjeJNZ/RuUd+1i2knr5igohM40zODdwqL6t3nFYI/NSAVcKtwf6MtHqfNA3cuZT3a0aa8QOycQcz/zRonAIS9rMeRBaDzY4VKSkqNXhsaQPGWrqGrOYtt1XSiysq6jyrc5cCEoorueq5sd2uKL8zR6weWggUyexnkciasdmxIq5tXQwf62n4WEvXRmXYxFsJgLvMVv52c1IZ9OGqEVqbmKBsbu5OznVewrpLKUlwZz2ILAabHSsyOPIRSPV88EkKCR38H0Q73wdMFJXx+bi0Rke1C+r/yAdGe3czejymNKKTr4Efrgo8+mBnE0Rk2wZ6dahznbK7NEIgwtvXBBERkSHY7FiRQc8OQHNXZyjquNVOaAXGzBxlwqiMT5IkTPYPrnOCskKS4OrohMc7+ZgsLlPo1soNoZ7t6jy7IwEY69Odz9oxAWd7BzzXLaDOMUpJgl9rdwS5y/e5T0TWhs2OFWnu4ozoLa9D1VIFSaH/4ae0u1PK5xc+g9CI3uYIz6iGdeiClwNDAaDahF0JQEsHR8SGRaC5vYMZojOu9/s/im6ubgD0V4i/2wAN8uqIvwX1M0Nktukl/954vOOdprrqz+LdrzqqXbBk4PB6z8ISkenwoYKwrocKAkBBTiF++eJX7Nu4H7nX8+GoaoaQiGA8OmkIOvXsYO7wjOpczg18ffEcfs1IQ5lWgzYqZzzZuTtGe3eV9aP6yzUaxP+Zgi3JZ5FyKxcKSYGebh54yqcHBrTrYNC8HjmwlAetCSFw9FoGtlw8i5M3MqERAh3VLhjn44dhHbrA0a5pH05vKXmbg63mbqt5A8Z5qCCbHVhfs1OVrf6DsNW8AeZui7nbat6A7eZuq3kDfIIyERERUYNxIVAisgqJ16/iWkkROrm7oqe66dadqiqzqBA5pcVwtndAJ7UL590QyQSbHSKyaMtPHcXXF8+itMpaU/YKBYZ16II3QwdCobj/E9SHMv/EujMncSbnhm5bu+ZqPN+tJ8Z06W4zc6KI5IrNDhFZrFd+/g4J169W216h1eK71GScz72Jz4f/3301PF8ln8HHJw5Dcc+TnDKLCvBh4kGcyc7CW30Hs+EhsmKcs0NEFumrC2dqbHSqSrmVh6WnjjT6GH/k3sTHJw4DuLPEQ1V3//Z92kV8k3K+0ccgIvNjs0NEFulf534zaNyulAuNPsaW5LP1LrQqAdh04XfwxlUi68Vmh4gsTqVWi+zSYoPGlmoqcSkvp1HH+Tk9td7lOASAKwX5yCgqaNQxiMj82OwQkcUpKm/YiuG5ZSWNOk5JA1aKL67gqvJE1orNDhFZHLVDw5b9eLCFS6OO08rR8JXJW3MVcyKrxWaHiCyOQqGAj0trg8a6Oarg6dy8UccZ5e1b711WCklCqEc7uLHZIbJabHaIyCK9GtTXoHFTe/Zp9DHG+vSAo9Kuzv8RaoXARL+gRh+DiMyPzQ4RWaQ+nu0wI/ChOsc84+uP0Z27NfoYHqrmWDb4cTja2Vc7w6OUJCggYV7oQPTxeKDRxyAi8+NDBYnIYkX1CESvNp745NRRnMnOghZ3bgX3cW2NaT1D0feB9vd9jEB3T2wZ+Qx2XPoD31w+j9zSEqjs7PFoh854yqcHvFu2uu9jEJF5sdkhIovm7+aBNUOfAGC8laDdnFR4MaA3Xgzo3WT7JCLLwctYREREJGtsdoiIiEjW2OwQERGRrLHZISIiIlljs0NERESyxmaHiIiIZI3NDhEREckamx0iIiKSNTY7REREJGtsdoiIiEjW2OwQERGRrLHZISIiIlljs0NERESyxmaHiIiIZI3NDhEREcmanbkDIGoojVaL1Lw82JfegkqrRCsHlblDIiIiC8Zmh6xGpVaLL8+fxubkM7hZUqzbHurRDi/4ByOoTVszRkdERJbK7Jex8vPz8fbbb2PgwIEIDg7Gc889h8TExFrH5+XlYdasWQgJCUFISAjeeustFBcX1zqe5KFSq8WsX/cgLumYXqMDAIk3MvHyT9/ixyuXzBQdERFZMrM3OzNnzkRSUhKWLl2Kr7/+Gn5+fpg8eTJSUlJqHP/KK68gPT0dGzZswD//+U8cOnQICxcuNHHUZGobzp1CwrUMiBpe0woBLQQWHPkF124Xmjw2IiKybGZtdq5cuYJDhw5h/vz56NOnD7y9vTFv3jx4eHjg22+/rTb+1KlTOHbsGBYtWgQ/Pz/069cP77zzDnbu3ImsrCwzZECmUKHR4KsLZ2psdKoSENh+6Q+TxERERNbDrHN2XF1dsXr1avj7++u2SZIEIQRu3bpVbXxiYiLc3d3RuXNn3bbQ0FBIkoQTJ05gxIgRjY7Fzs7sJ7kaRalU6P1Xjn7PycKt8rJ6x2mFwL70y3ild18TRGU+tlDz2thq7raaN2C7udtq3oBxcjdrs6NWqzFo0CC9bbt378aff/6JAQMGVBuflZWFtm31J6E6ODjAxcUF165da3QcCoUEV1fnRn+/JVCrncwdgvHkG/4DX1xZafW1NJSsa14PW83dVvMGbDd3W80baNrcLepurBMnTmDu3LkIDw/HkCFDqr1eUlICBweHatubNWuGsrL6f/OvjVYrUFBgnZOclUoF1GonFBSUQKPRmjsco1BplQaNkwC4OTohL++2cQMyM1uoeW1sNXdbzRuw3dxtNW/A8NzVaieDz/5YTLMTHx+P119/HYGBgVi6dGmNYxwdHVFeXl5te1lZGVSq+3vWSmWldf8waTRaq8+hNt4tXNFR7YIrBfn1ztsZ5d1Ntu/DveRc8/rYau62mjdgu7nbat5A0+ZuERcDP//8c8yYMQMDBw7EmjVr4OjoWOM4T09P3LhxQ29beXk58vPz4eHhYYpQyQwkScJk/+A6Gx2FJMHV0QmPd/IxWVxERGQdzN7sbNq0Ce+++y6ef/55LFu2rMbLVHeFhITg+vXruHLlim5bQkICACA4ONjosZL5DOvQBS8HhgIAlJKk95oEoKWDI2LDItDcvvafHyIisk1mvYyVmpqK999/H0OHDsXUqVORk5Oje83R0REqlQq5ublo0aIFHB0dERgYiODgYLz22mtYsGABiouLMX/+fDz55JM8s2MDJvbohRCPB/D1xXP4NSMNZVoN2qic8WTn7hjt3RUtm9V8RpCIiGybWZudH374ARUVFdi7dy/27t2r99qYMWMwffp0hIeHY9GiRRg7diwkSUJsbCwWLlyIiRMnolmzZhg+fDiio6PNlAGZWo/WbfB26zaws1PA1dUZeXm3bfZ6NhERGUYSQtQ351P2NBotcnOt8w4eW/3Qt9W8AeZui7nbat6A7eZuq3kDhufeqpWzwXdjmX3ODhEREZExsdkhIiIiWWOzQ0RERLLGZoeIiIhkjc0OERERyRqbHSIiIpI1NjtEREQka2x2iIiISNb4UEEAQghotdb7NiiVCmg0tvXQKcB28waYuy3mbqt5A7abu63mDRiWu0IhQbpnrcTasNkhIiIiWeNlLCIiIpI1NjtEREQka2x2iIiISNbY7BAREZGssdkhIiIiWWOzQ0RERLLGZoeIiIhkjc0OERERyRqbHSIiIpI1NjtEREQka2x2iIiISNbY7BAREZGssdkhIiIiWWOzYyVSU1MRFBSEbdu21Tpm+/bt6Nq1a7U/V65cMWGkTePq1as15rJly5Yax+fl5WHWrFkICQlBSEgI3nrrLRQXF5s46qbR0NzlVHcA2LFjB0aMGIGAgABERERg9+7dtY6VU90bkrdcap6QkFBjHl27dkV4eHiN3yOXmjcmd7nUvaKiAjExMRg8eDCCgoIQGRmJkydP1jq+SWouyOKVl5eLsWPHCl9fX7F169Zaxy1atEhERUWJGzdu6P2prKw0YbRN46effhIBAQEiKytLL5eSkpIax0dFRYlx48aJM2fOiMOHD4uwsDAxe/ZsE0fdNBqau5zqvmPHDtG9e3exYcMGkZaWJmJjY0W3bt3EyZMnaxwvl7o3NG+51LysrKxaDgcPHhQ9evQQX331VY3fI5eaNyZ3udT9k08+Ef379xcHDhwQaWlpYt68eSI4OFhcv369xvFNUXM2O1bg448/FuPHj6+32fnLX/4i/vGPf5gwMuNZuXKlGD16tEFjT548KXx9fcWlS5d02w4cOCC6du1a6z8eS9aQ3IWQT921Wq0ICwsTixcv1tv+wgsviE8//bTaeLnUvaF5CyGfmt+rvLxcREREiFdffbXG1+VS85rUl7sQ8qn76NGjxaJFi3R/LywsFL6+vmLPnj3VxjZVzXkZy8IdP34cmzdvxgcffFDv2AsXLqBLly4miMr4GpJLYmIi3N3d0blzZ9220NBQSJKEEydOGCtEo2loHeVS98uXL+Pq1asYNWqU3va1a9di6tSp1cbLpe4NzRuQT83v9cUXX+DatWuIjo6u8XW51Lwm9eUOyKfuLi4u+Pnnn5GRkQGNRoPNmzfDwcEB3bt3rza2qWrOZseCFRQUYPbs2XjzzTfRtm3bOsfm5uYiOzsbx48fx8iRIzFgwABMmzYNqampJoq2aSUnJyMnJweRkZF4+OGH8dxzz+HAgQM1js3Kyqr2/jg4OMDFxQXXrl0zRbhNqiG5y6nuaWlpAIDi4mJMnjwZ/fr1w7hx47Bv374ax8ul7g3NW041r6qsrAyffvopJk6ciDZt2tQ4Ri41v5chucup7vPmzYOdnR3Cw8MREBCAmJgYLFu2DA8++GC1sU1VczY7FmzBggXo1atXtd/4apKcnAwAUCqV+OCDDxATE4Pi4mJERkYiOzvb2KE2qfLycqSlpaGoqAivvvoqVq9ejYCAALz00ks4cuRItfElJSVwcHCotr1Zs2YoKyszRchNpqG5y6nuRUVFAIA5c+Zg5MiRWLduHfr374+XX35Z1nVvaN5yqnlVO3fuRFlZGcaPH1/rGLnU/F6G5C6nuqekpECtViMuLg6bN2/G2LFjMWfOHJw/f77a2Kaqud19RUxGs2PHDiQmJmLXrl0Gje/bty+OHTuGli1b6rbFxcUhLCwM27Ztw5QpU4wVapNzcHDA8ePHYWdnp/sh9/f3R0pKCtauXYt+/frpjXd0dER5eXm1/ZSVlUGlUpkk5qbS0NzlVHd7e3sAwOTJkzFmzBgAQPfu3XHu3DmsX79etnVvaN5yqnlVO3bswLBhw+Dq6lrrGLnU/F6G5C6Xul+9ehV///vfsWHDBvTp0wcAEBAQgEuXLmH58uWIi4vTG99UNeeZHQu1detW5OTk6G7NCwoKAgDMnz8fERERNX5P1X8EAKBSqeDl5YWsrCyjx9vUVCpVtW7e19e3xlw8PT1x48YNvW3l5eXIz8+Hh4eHUeM0hobkDsin7p6engDu5FpVly5dkJGRUeN4OdS9oXkD8qn5Xbm5uTh16hRGjBhR5zi51LwqQ3MH5FH306dPo6KiAgEBAXrbAwMDdZd0q2qqmrPZsVBLlizB999/jx07duj+AMArr7yC1atXVxu/adMmPPTQQygtLdVtKyoqQlpamtVNaDt//jyCgoKQmJiot/3MmTM15hISEoLr16/rPWsiISEBABAcHGzcYJtYQ3OXU9179OgBZ2dnJCUl6W1PTk6u8Vq+XOre0LzlVPO7Tp48CUmSEBoaWuc4udS8KkNzl0vd786/uXDhgt725ORkdOjQodr4Jqv5/d1ARqZU9dbzyspKvWevZGZmipCQEDFjxgyRnJwsTp8+LSZNmiQeffTRWp/PYqk0Go0YN26cGDlypDh+/Li4dOmSeP/994W/v784f/58tdy1Wq149tlnxZgxY0RSUpI4cuSICAsLE2+88YaZM2m4huYup7oLIURcXJwICgoSu3btEleuXBErVqwQ3bp1E0ePHpV13RuSt9xqLoQQy5cvF8OGDau2Xc41v8vQ3OVSd41GIyIjI8Xw4cPFkSNHRGpqqoiJiRHdu3cXp06dMlrN2exYkarNTnp6erXn7pw7d0688MILonfv3iI4OFjMmDFDZGZmmivc+5KTkyOio6NF//79RUBAgHjmmWfE8ePHhRA1556dnS1mzJghevXqJR566CExf/58UVpaaq7w70tDc5dT3YUQYt26dWLIkCHCz89PjB49Wuzdu1cIIf+6NyRvudV8/vz54umnn662Xe41F6Jhucul7vn5+WLBggVi8ODBIigoSDzzzDMiISFBCGG8mktCCNGIM1FEREREVoFzdoiIiEjW2OwQERGRrLHZISIiIlljs0NERESyxmaHiIiIZI3NDhEREckamx0iIiKSNTY7REQ14CPIiOSDzQ4RmdyQIUPwxhtv1Pr68uXL0bVrVxNGpG/Lli344IMPdH/ftm0bunbtWuvCnPXJyMjA4MGDkZub21Qh1igmJgYLFy406jGIrBGbHSKie6xcuRL5+flNsi8hBObOnYuJEyeiVatWTbLP2kydOhXx8fE4cuSIUY9DZG3Y7BARGdHevXtx/vx5REZGGv1YKpUKEyZMwOLFi41+LCJrwmaHyAacPXsWEydORO/evREUFIRJkyYhKSlJb0xiYiKioqIQGBiI0NBQzJkzR++yy91LOUlJSRgzZgx69uyJUaNG4fvvv9fbT0ZGBmbPno0BAwbAz88P/fr1w+zZs5GXl3dfOcTHx2Ps2LEICAhA//798Y9//APFxcW615cvX46hQ4fil19+wahRo+Dv74/HHnsM27dv19tPSkoKXnrpJQQHB+Phhx9GTEwMoqOjMX78eAB3LrFdvXoV27dvr3bpKikpCc8++ywCAgIwePBgrF27tt64V61ahWHDhqFZs2a6bRUVFYiLi8Ojjz6Knj17IiIiAlu3btW9Pn78eLz99ttYuXIlHnnkEQQGBuKll15CdnY2tm7diqFDh+rqeO+ltVGjRuHChQvYv39/w95gIhljs0Mkc0VFRXjxxRfh6uqKf/7zn4iJiUFJSQkmT56MwsJCAMDx48cxadIkODo6YtmyZZg7dy6OHTuGCRMmoLS0VG9/U6dORXh4OGJjY9GpUyfMnDkTP/30EwCgpKQEEyZMQEpKCubPn4+1a9ciKioK3377LZYuXdroHHbt2oVp06bB29sbcXFxmD59Or755hu8/PLLehOJb968iXfeeQcTJkzA6tWr4eXlhTfeeAMpKSkAgNzcXERFReHatWtYtGgR3nzzTezZswfffvutbh+xsbFwd3fHoEGDsHnzZrRp00b32oIFCzBy5EisWrUKPXv2xIcffoiff/651rgvX76MM2fOYPjw4Xrb58yZg9WrV+Opp57CqlWrMGjQIMydOxc7duzQjfnuu+9w+PBhvPfee4iOjsbhw4cRFRWFjRs3Ys6cOZg3bx6SkpLwzjvv6O3b09MTQUFB+Oabbxr1XhPJkZ25AyAi47p06RJyc3Mxfvx49O7dGwDg7e2N//znPygqKkKLFi3w8ccfo1OnTli1ahWUSiUAIDAwUHfG4fnnn9ftLyoqCtOnTwcAPPLIIxgzZgxWrFiB8PBwpKWlwdPTE4sXL8aDDz4IAOjbty9+//13HDt2rFHxCyGwZMkSPPLII1iyZIlue8eOHTFp0iTs378fgwcPBnCn2XrvvffQr18/3ZiwsDDs378fnTt3xsaNG3H79m3s2LEDHh4eujwfe+wx3X579OgBBwcHtGrVCr169dKLZebMmXjuuecAAL169cK+fftw9OhRhIWF1Rj70aNHAQA9e/bUbbt48SK+++47zJs3DxMmTAAA9OvXD5mZmUhISMCTTz4J4M7Zn9jYWLRs2RLAncthBw8eRHx8PNq3bw8A+OOPP7Bz585qxw0ICNBr4IhsHc/sEMmcj48PWrVqhb/+9a+YP38+9u3bB3d3d8yePRtt27ZFSUkJkpKSMGjQIAghUFlZicrKSrRv3x6dO3fGoUOH9Pb3xBNP6L6WJAlDhw7F2bNnUVJSgu7du2PTpk3w8vJCeno6Dhw4gHXr1uHy5cuoqKhoVPyXL1/G9evXMWTIEF1slZWVCAkJQfPmzavFV7VB8fT0BADd5a6jR48iKChI1+gAQLt27RAUFGRQLH369NF9rVKp4ObmhoKCglrHp6enQ61WQ61W67YlJiYCAIYOHao3dtmyZVi0aJHu7507d9Y1OgDg7u6OVq1a6RodAHBxcdGdnauqXbt2yMnJQUlJiUF5Eckdz+wQyZyzszO++OILrFy5Et9//z3+85//wMnJCaNHj8a8efNQUFAArVaLNWvWYM2aNdW+v+pcEwB6jQIAtG7dGkIIFBYWwsnJCevXr8eqVauQl5cHNzc3+Pn5wcnJqcYPZUPcvStq4cKFNd5WfePGDb2/Ozk56b5WKO78Pnf3Uldubi78/Pyq7cPd3R03b96sN5aq+767/7qex1NUVFTte+7m07p16zqP1bx583qPXxuVSgUAupoQ2To2O0Q2wNvbGx999BE0Gg1Onz6NnTt34ssvv4SXlxciIyMhSRImTZqEiIiIat9774dlXl6eXsOTnZ0NpVIJFxcX7Nq1C4sXL8asWbPw1FNP6W61/tvf/obff/+9UbHfPSsye/ZshIaGVnu96tmP+nh6eiInJ6fa9pq2NQVXV9dqTd7dfHJzc3VnnoA7Z7Byc3P1zh411q1btyBJElxcXO57X0RywMtYRDK3Z88e9O3bFzdv3oRSqURQUBAWLFgAtVqN69evo3nz5ujRowcuX76MgIAA3R8fHx/ExsYiISFBb3/79u3TfS2EwI8//ojevXvDwcEBJ06cQIsWLTBlyhRdo3P79m2cOHECWq22UfF7e3ujdevWyMjI0IvP09MTH3/8Mc6dO2fwvkJCQnDq1Cm9szg3b97Eb7/9pjfu7hmh+/XAAw+guLgYt27d0m27O28qPj5eb2xMTAzefffdJjnu9evX4ebmBgcHhybZH5G145kdIpkLDg6GVqvFtGnTMGXKFDg7O2P37t0oLCzEsGHDANyZeDtlyhTMmjULo0ePhkajwbp165CUlIS//vWvevv76KOPUF5ejk6dOmHLli1ISUnBv/71LwB3JuJ++eWXWLx4McLCwnDjxg2sXbsW2dnZDToDU5VSqcRrr72Gt99+G0qlEmFhYSgoKMCKFSuQlZVV42Wp2kyYMAFffPEFJk+ejGnTpgEA4uLiUF5eDkmSdOPUajXOnTuHY8eO6U0ubqj+/fsDAE6ePKmbxNytWzcMHz4cS5YsQWlpKfz8/HDw4EHs3bsXy5Yta/Sxqjpx4gQeeeSRJtkXkRyw2SGSuTZt2uCzzz7DJ598gnnz5qGkpAQ+Pj5Yvnw5+vbtCwAYMGAA1q5di9jYWLzyyiuwt7eHn58f1q9fX+2OpAULFmDVqlVIT09Hjx49sG7dOt2llzFjxiAjIwNbt27Fpk2b4OHhgUGDBiEyMhJvvfUWLl26hC5dujQ4h3HjxsHZ2RmfffYZNm/eDJVKheDgYCxZskRvwm591Go1/v3vf+O9997D7Nmz4ezsjMjISKhUKt08FwB44YUX8P7772Py5MlYv359g+O9q3379vDz88P+/fv17tj66KOPEBsbi40bNyIvLw+dOnXCsmXLqt2i3hhZWVk4f/48Xn311fveF5FcSIKr3RGRAbZt24bo6Gj89NNP8PLyMnc4jZKUlIT8/HwMGjRIt62yshKDBw9GREQEoqOjm/yYP/zwA+bOnYsDBw7oNVTGEhsbi/j4eGzfvl3vbBWRLeOcHSKyGZmZmZg6dSqWL1+OhIQE/PLLL5g+fToKCwvx9NNPG+WYw4YNg4+PDzZt2mSU/VdVVFSEL7/8EjNnzmSjQ1QFL2MRkc14/PHHkZ+fj02bNmHt2rWwt7dHYGAgPv/8c3Tu3Nkox5QkCR9++CGioqIwduxYoy4GumrVKoSHh2PgwIFGOwaRNeJlLCIiIpI1XsYiIiIiWWOzQ0RERLLGZoeIiIhkjc0OERERyRqbHSIiIpI1NjtEREQka2x2iIiISNbY7BAREZGs/T/fGRxRTR97wQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score, adjusted_rand_score\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 1. 加载数据集\n",
    "iris = datasets.load_iris()\n",
    "X = iris.data\n",
    "y_true = iris.target \n",
    "\n",
    "# 2. 选择聚类的数目\n",
    "n_clusters = 3\n",
    "\n",
    "# 3. 应用KMeans聚类算法\n",
    "kmeans = KMeans(n_clusters=n_clusters, random_state=110)\n",
    "kmeans.fit(X)\n",
    "y_kmeans = kmeans.predict(X)\n",
    "\n",
    "# 4. 评估聚类结果\n",
    "# 计算轮廓系数，衡量聚类的质量\n",
    "silhouette_avg = silhouette_score(X, y_kmeans)\n",
    "print(f\"Silhouette Score: {silhouette_avg}\")\n",
    "\n",
    "# 计算调整后的Rand指数，衡量聚类结果与真实标签的一致性\n",
    "ari = adjusted_rand_score(iris.target, y_kmeans)\n",
    "print(f\"Adjusted Rand Index: {ari}\")\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# 可视化聚类结果（只展示前两个特征）\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y_kmeans, s=50, cmap='viridis')\n",
    "plt.xlabel(iris.feature_names[0])\n",
    "plt.ylabel(iris.feature_names[1])\n",
    "plt.title('KMeans Clustering on Iris Dataset')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "9211ced2-bd6f-4d67-979c-c2c395573318",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      setosa       1.00      1.00      1.00        50\n",
      "  versicolor       0.77      0.96      0.86        50\n",
      "   virginica       0.95      0.72      0.82        50\n",
      "\n",
      "    accuracy                           0.89       150\n",
      "   macro avg       0.91      0.89      0.89       150\n",
      "weighted avg       0.91      0.89      0.89       150\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Classification Report:\")\n",
    "print(classification_report(y_true, y_kmeans, target_names=iris.target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d0ddcbeb-0acf-4b01-b060-3ed442f520d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_features=3, n_estimators=50)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(max_features=3, n_estimators=50)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_features=3, n_estimators=50)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split( X, y , test_size=0.3, random_state=420)\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "# 通过RandomForestClassifier类定义一个随机森林模型，名字叫rf\n",
    "rf = RandomForestClassifier(n_estimators = 100)\n",
    "# 对lr模型进行训练(fit)\n",
    "rf.fit(X_train, y_train)\n",
    "# 用最佳参数重新训练模型\n",
    "rf = RandomForestClassifier(criterion='gini',max_features=3,n_estimators=50)\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "becd1ade-4b58-4524-aab9-ff84478265a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "分类评估汇总报告:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        15\n",
      "           1       1.00      0.90      0.95        20\n",
      "           2       0.83      1.00      0.91        10\n",
      "\n",
      "    accuracy                           0.96        45\n",
      "   macro avg       0.94      0.97      0.95        45\n",
      "weighted avg       0.96      0.96      0.96        45\n",
      "\n",
      "误分类矩阵:\n",
      " [[15  0  0]\n",
      " [ 0 18  2]\n",
      " [ 0  0 10]]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import auc\n",
    "# 利用模型对测试集进行预测，输出target预测标签值和概率\n",
    "y_test_pred = rf.predict(X_test)\n",
    "y_test_prob = rf.predict_proba(X_test)\n",
    "# 分类评估汇总报告classification_report\n",
    "print('分类评估汇总报告:\\n',classification_report(y_test,y_test_pred))\n",
    "# 误分类矩阵 confusion_matrix\n",
    "print('误分类矩阵:\\n',confusion_matrix(y_test,y_test_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "357857df-d2be-422e-8b45-ea661e86a57c",
   "metadata": {},
   "source": [
    "# Homework"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3b0729c8-0f90-42ad-b3da-d6565e2618bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>3</td>\n",
       "      <td>13.71</td>\n",
       "      <td>5.65</td>\n",
       "      <td>2.45</td>\n",
       "      <td>20.5</td>\n",
       "      <td>95</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.06</td>\n",
       "      <td>7.70</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.74</td>\n",
       "      <td>740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>3</td>\n",
       "      <td>13.40</td>\n",
       "      <td>3.91</td>\n",
       "      <td>2.48</td>\n",
       "      <td>23.0</td>\n",
       "      <td>102</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.41</td>\n",
       "      <td>7.30</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.56</td>\n",
       "      <td>750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>3</td>\n",
       "      <td>13.27</td>\n",
       "      <td>4.28</td>\n",
       "      <td>2.26</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.35</td>\n",
       "      <td>10.20</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.56</td>\n",
       "      <td>835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>3</td>\n",
       "      <td>13.17</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.37</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.46</td>\n",
       "      <td>9.30</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.62</td>\n",
       "      <td>840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>3</td>\n",
       "      <td>14.13</td>\n",
       "      <td>4.10</td>\n",
       "      <td>2.74</td>\n",
       "      <td>24.5</td>\n",
       "      <td>96</td>\n",
       "      <td>2.05</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.35</td>\n",
       "      <td>9.20</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.60</td>\n",
       "      <td>560</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     0      1     2     3     4    5     6     7     8     9      10    11  \\\n",
       "0     1  14.23  1.71  2.43  15.6  127  2.80  3.06  0.28  2.29   5.64  1.04   \n",
       "1     1  13.20  1.78  2.14  11.2  100  2.65  2.76  0.26  1.28   4.38  1.05   \n",
       "2     1  13.16  2.36  2.67  18.6  101  2.80  3.24  0.30  2.81   5.68  1.03   \n",
       "3     1  14.37  1.95  2.50  16.8  113  3.85  3.49  0.24  2.18   7.80  0.86   \n",
       "4     1  13.24  2.59  2.87  21.0  118  2.80  2.69  0.39  1.82   4.32  1.04   \n",
       "..   ..    ...   ...   ...   ...  ...   ...   ...   ...   ...    ...   ...   \n",
       "173   3  13.71  5.65  2.45  20.5   95  1.68  0.61  0.52  1.06   7.70  0.64   \n",
       "174   3  13.40  3.91  2.48  23.0  102  1.80  0.75  0.43  1.41   7.30  0.70   \n",
       "175   3  13.27  4.28  2.26  20.0  120  1.59  0.69  0.43  1.35  10.20  0.59   \n",
       "176   3  13.17  2.59  2.37  20.0  120  1.65  0.68  0.53  1.46   9.30  0.60   \n",
       "177   3  14.13  4.10  2.74  24.5   96  2.05  0.76  0.56  1.35   9.20  0.61   \n",
       "\n",
       "       12    13  \n",
       "0    3.92  1065  \n",
       "1    3.40  1050  \n",
       "2    3.17  1185  \n",
       "3    3.45  1480  \n",
       "4    2.93   735  \n",
       "..    ...   ...  \n",
       "173  1.74   740  \n",
       "174  1.56   750  \n",
       "175  1.56   835  \n",
       "176  1.62   840  \n",
       "177  1.60   560  \n",
       "\n",
       "[178 rows x 14 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "%matplotlib inline\n",
    "from sklearn import datasets\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "dataset = pd.read_csv('wine.txt',header=None)\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "dccf37e5-e0da-4d3c-a586-5187b026e31c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0\n",
       "2    71\n",
       "1    59\n",
       "3    48\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=dataset.iloc[:,1:]\n",
    "X\n",
    "y=dataset.iloc[:,0]\n",
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "61581623-8520-4f91-9d51-8b465755a0c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>principal component 1</th>\n",
       "      <th>principal component 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.316751</td>\n",
       "      <td>-1.443463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.209465</td>\n",
       "      <td>0.333393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.516740</td>\n",
       "      <td>-1.031151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.757066</td>\n",
       "      <td>-2.756372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.008908</td>\n",
       "      <td>-0.869831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <th>173</th>\n",
       "      <td>-3.370524</td>\n",
       "      <td>-2.216289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>-2.601956</td>\n",
       "      <td>-1.757229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>-2.677839</td>\n",
       "      <td>-2.760899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>-2.387017</td>\n",
       "      <td>-2.297347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>-3.208758</td>\n",
       "      <td>-2.768920</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     principal component 1  principal component 2\n",
       "0                 3.316751              -1.443463\n",
       "1                 2.209465               0.333393\n",
       "2                 2.516740              -1.031151\n",
       "3                 3.757066              -2.756372\n",
       "4                 1.008908              -0.869831\n",
       "..                     ...                    ...\n",
       "173              -3.370524              -2.216289\n",
       "174              -2.601956              -1.757229\n",
       "175              -2.677839              -2.760899\n",
       "176              -2.387017              -2.297347\n",
       "177              -3.208758              -2.768920\n",
       "\n",
       "[178 rows x 2 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = StandardScaler().fit_transform(X) #标准化\n",
    "pca = PCA(n_components=2) #pca降维到2\n",
    "principalComponents = pca.fit_transform(X)\n",
    "principalDf = pd.DataFrame(data = principalComponents\n",
    "             , columns = ['principal component 1', 'principal component 2'])\n",
    "principalDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "93abc7da-a9fb-43b0-bdc3-1ed41947aedd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy\n",
    "from scipy.cluster import hierarchy\n",
    "dendro=hierarchy.dendrogram(hierarchy.linkage(X,method='ward')) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d082c405-8840-469c-bf66-54f0af665094",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'wcss: sum of dist. of sample to their closest cluster center')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# 肘部法则（Elbow method）来寻找最优的聚类个数\n",
    "from sklearn.cluster import KMeans\n",
    "wcss=[]\n",
    "for i in range(1,10):\n",
    "    kmeans=KMeans(n_clusters=i,init='k-means++',)\n",
    "    kmeans.fit(X)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "    \n",
    "plt.plot(range(1,10),wcss)\n",
    "plt.title('Elbow Method')\n",
    "plt.xlabel('No. of cluster')\n",
    "plt.ylabel('wcss: sum of dist. of sample to their closest cluster center' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d5bc993d-5892-487e-b179-fff2a7b5b4c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 1 2 2 2 2 2 2 2 0 2 2 2 0\n",
      " 2 2 2 2 0 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 1 2 2 0 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]\n",
      "178\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.78092774, -0.32927816,  0.29430757, -0.62579582,  0.6555505 ,\n",
       "         0.83653747,  0.93269672, -0.57394892,  0.61904614,  0.1395866 ,\n",
       "         0.48831326,  0.75835235,  1.08874017],\n",
       "       [ 0.16490746,  0.87154706,  0.18689833,  0.52436746, -0.07547277,\n",
       "        -0.97933029, -1.21524764,  0.72606354, -0.77970639,  0.94153874,\n",
       "        -1.16478865, -1.29241163, -0.40708796],\n",
       "       [-0.92681994, -0.3710333 , -0.45027776,  0.21124115, -0.60485907,\n",
       "        -0.05702466,  0.03627047, -0.00470649,  0.00231861, -0.90400029,\n",
       "         0.44685988,  0.27584829, -0.77647436]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans_1=KMeans(n_clusters=3)\n",
    "kmeans_1.fit(X)\n",
    "cluster_pred=kmeans_1.predict(X)\n",
    "print(cluster_pred)\n",
    "print(len(cluster_pred))\n",
    "cluster_pred_2=kmeans_1.labels_\n",
    "cluster_center=kmeans_1.cluster_centers_\n",
    "\n",
    "# 类中心点\n",
    "cluster_center"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "2c4f9224-fcd4-4797-8779-1420aaa3d663",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 1, 2, 2, 2, 2,\n",
       "       2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 1, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 聚类结果\n",
    "cluster_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "8e779581-4424-4113-8007-04a2229133a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix\n",
    "def show_confusion_matrix(cnf_matrix, class_labels):\n",
    "    plt.matshow(cnf_matrix, cmap=plt.cm.YlGn, alpha=0.7)\n",
    "    ax = plt.gca()\n",
    "    ax.set_xlabel('Predicted Label', fontsize=16)\n",
    "    ax.set_xticks(range(0,len(class_labels)))\n",
    "    ax.set_xticklabels(class_labels,rotation=45)\n",
    "    ax.set_ylabel('Actual Label', fontsize=16, rotation=90)\n",
    "    ax.set_yticks(range(0,len(class_labels)))\n",
    "    ax.set_yticklabels(class_labels)\n",
    "    ax.xaxis.set_label_position('top')\n",
    "    ax.xaxis.tick_top()\n",
    "\n",
    "    for row in range(len(cnf_matrix)):\n",
    "        for col in range(len(cnf_matrix[row])):\n",
    "            ax.text(col, row, cnf_matrix[row][col], va='center', ha='center', fontsize=32)\n",
    "\n",
    "class_labels = [0,1,2,3]\n",
    "\n",
    "cnf_matrix = confusion_matrix(y, cluster_pred) \n",
    "show_confusion_matrix(cnf_matrix, class_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "9e345083-56fa-489d-9e22-30fb94ad495f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9550561797752809\n"
     ]
    }
   ],
   "source": [
    "print((59+48+63)/178)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a621a19-c1f6-4fed-9643-ea5a1391b9da",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
